diff --git a/codes/go/chapter_dynamic_programming/climbing_stairs_backtrack.go b/codes/go/chapter_dynamic_programming/climbing_stairs_backtrack.go
new file mode 100644
index 000000000..8049775d5
--- /dev/null
+++ b/codes/go/chapter_dynamic_programming/climbing_stairs_backtrack.go
@@ -0,0 +1,36 @@
+// File: climbing_stairs_backtrack.go
+// Created Time: 2023-07-18
+// Author: Reanon (793584285@qq.com)
+
+package chapter_dynamic_programming
+
+/* 回溯 */
+func backtrack(choices []int, state, n int, res []int) {
+	// 当爬到第 n 阶时，方案数量加 1
+	if state == n {
+		res[0] = res[0] + 1
+	}
+	// 遍历所有选择
+	for _, choice := range choices {
+		// 剪枝：不允许越过第 n 阶
+		if state+choice > n {
+			break
+		}
+		// 尝试：做出选择，更新状态
+		backtrack(choices, state+choice, n, res)
+		// 回退
+	}
+}
+
+/* 爬楼梯：回溯 */
+func climbingStairsBacktrack(n int) int {
+	// 可选择向上爬 1 或 2 阶
+	choices := []int{1, 2}
+	// 从第 0 阶开始爬
+	state := 0
+	res := make([]int, 1)
+	// 使用 res[0] 记录方案数量
+	res[0] = 0
+	backtrack(choices, state, n, res)
+	return res[0]
+}
diff --git a/codes/go/chapter_dynamic_programming/climbing_stairs_constraint_dp.go b/codes/go/chapter_dynamic_programming/climbing_stairs_constraint_dp.go
new file mode 100644
index 000000000..2621b3939
--- /dev/null
+++ b/codes/go/chapter_dynamic_programming/climbing_stairs_constraint_dp.go
@@ -0,0 +1,25 @@
+// File: climbing_stairs_constraint_dp.go
+// Created Time: 2023-07-18
+// Author: Reanon (793584285@qq.com)
+
+package chapter_dynamic_programming
+
+/* 带约束爬楼梯：动态规划 */
+func climbingStairsConstraintDP(n int) int {
+	if n == 1 || n == 2 {
+		return n
+	}
+	// 初始化 dp 表，用于存储子问题的解
+	dp := make([][3]int, n+1)
+	// 初始状态：预设最小子问题的解
+	dp[1][1] = 1
+	dp[1][2] = 0
+	dp[2][1] = 0
+	dp[2][2] = 1
+	// 状态转移：从较小子问题逐步求解较大子问题
+	for i := 3; i <= n; i++ {
+		dp[i][1] = dp[i-1][2]
+		dp[i][2] = dp[i-2][1] + dp[i-2][2]
+	}
+	return dp[n][1] + dp[n][2]
+}
diff --git a/codes/go/chapter_dynamic_programming/climbing_stairs_dfs.go b/codes/go/chapter_dynamic_programming/climbing_stairs_dfs.go
new file mode 100644
index 000000000..62310483e
--- /dev/null
+++ b/codes/go/chapter_dynamic_programming/climbing_stairs_dfs.go
@@ -0,0 +1,21 @@
+// File: climbing_stairs_dfs.go
+// Created Time: 2023-07-18
+// Author: Reanon (793584285@qq.com)
+
+package chapter_dynamic_programming
+
+/* 搜索 */
+func dfs(i int) int {
+	// 已知 dp[1] 和 dp[2] ，返回之
+	if i == 1 || i == 2 {
+		return i
+	}
+	// dp[i] = dp[i-1] + dp[i-2]
+	count := dfs(i-1) + dfs(i-2)
+	return count
+}
+
+/* 爬楼梯：搜索 */
+func climbingStairsDFS(n int) int {
+	return dfs(n)
+}
diff --git a/codes/go/chapter_dynamic_programming/climbing_stairs_dfs_mem.go b/codes/go/chapter_dynamic_programming/climbing_stairs_dfs_mem.go
new file mode 100644
index 000000000..03d5158e5
--- /dev/null
+++ b/codes/go/chapter_dynamic_programming/climbing_stairs_dfs_mem.go
@@ -0,0 +1,32 @@
+// File: climbing_stairs_dfs_mem.go
+// Created Time: 2023-07-18
+// Author: Reanon (793584285@qq.com)
+
+package chapter_dynamic_programming
+
+/* 记忆化搜索 */
+func dfsMem(i int, mem []int) int {
+	// 已知 dp[1] 和 dp[2] ，返回之
+	if i == 1 || i == 2 {
+		return i
+	}
+	// 若存在记录 dp[i] ，则直接返回之
+	if mem[i] != -1 {
+		return mem[i]
+	}
+	// dp[i] = dp[i-1] + dp[i-2]
+	count := dfsMem(i-1, mem) + dfsMem(i-2, mem)
+	// 记录 dp[i]
+	mem[i] = count
+	return count
+}
+
+/* 爬楼梯：记忆化搜索 */
+func climbingStairsDFSMem(n int) int {
+	// mem[i] 记录爬到第 i 阶的方案总数，-1 代表无记录
+	mem := make([]int, n+1)
+	for i := range mem {
+		mem[i] = -1
+	}
+	return dfsMem(n, mem)
+}
diff --git a/codes/go/chapter_dynamic_programming/climbing_stairs_dp.go b/codes/go/chapter_dynamic_programming/climbing_stairs_dp.go
new file mode 100644
index 000000000..4cb060b9b
--- /dev/null
+++ b/codes/go/chapter_dynamic_programming/climbing_stairs_dp.go
@@ -0,0 +1,35 @@
+// File: climbing_stairs_dp.go
+// Created Time: 2023-07-18
+// Author: Reanon (793584285@qq.com)
+
+package chapter_dynamic_programming
+
+/* 爬楼梯：动态规划 */
+func climbingStairsDP(n int) int {
+	if n == 1 || n == 2 {
+		return n
+	}
+	// 初始化 dp 表，用于存储子问题的解
+	dp := make([]int, n+1)
+	// 初始状态：预设最小子问题的解
+	dp[1] = 1
+	dp[2] = 2
+	// 状态转移：从较小子问题逐步求解较大子问题
+	for i := 3; i <= n; i++ {
+		dp[i] = dp[i-1] + dp[i-2]
+	}
+	return dp[n]
+}
+
+/* 爬楼梯：状态压缩后的动态规划 */
+func climbingStairsDPComp(n int) int {
+	if n == 1 || n == 2 {
+		return n
+	}
+	a, b := 1, 2
+	// 状态转移：从较小子问题逐步求解较大子问题
+	for i := 3; i <= n; i++ {
+		a, b = b, a+b
+	}
+	return b
+}
diff --git a/codes/go/chapter_dynamic_programming/climbing_stairs_test.go b/codes/go/chapter_dynamic_programming/climbing_stairs_test.go
new file mode 100644
index 000000000..7599e11ca
--- /dev/null
+++ b/codes/go/chapter_dynamic_programming/climbing_stairs_test.go
@@ -0,0 +1,57 @@
+// File: climbing_stairs_test.go
+// Created Time: 2023-07-18
+// Author: Reanon (793584285@qq.com)
+
+package chapter_dynamic_programming
+
+import (
+	"fmt"
+	"testing"
+)
+
+func TestClimbingStairsBacktrack(t *testing.T) {
+	n := 9
+	res := climbingStairsBacktrack(n)
+	fmt.Printf("爬 %d 阶楼梯共有 %d 种方案\n", n, res)
+}
+
+func TestClimbingStairsDFS(t *testing.T) {
+	n := 9
+	res := climbingStairsDFS(n)
+	fmt.Printf("爬 %d 阶楼梯共有 %d 种方案\n", n, res)
+}
+
+func TestClimbingStairsDFSMem(t *testing.T) {
+	n := 9
+	res := climbingStairsDFSMem(n)
+	fmt.Printf("爬 %d 阶楼梯共有 %d 种方案\n", n, res)
+}
+
+func TestClimbingStairsDP(t *testing.T) {
+	n := 9
+	res := climbingStairsDP(n)
+	fmt.Printf("爬 %d 阶楼梯共有 %d 种方案\n", n, res)
+}
+
+func TestClimbingStairsDPComp(t *testing.T) {
+	n := 9
+	res := climbingStairsDPComp(n)
+	fmt.Printf("爬 %d 阶楼梯共有 %d 种方案\n", n, res)
+}
+
+func TestClimbingStairsConstraintDP(t *testing.T) {
+	n := 9
+	res := climbingStairsConstraintDP(n)
+	fmt.Printf("爬 %d 阶楼梯共有 %d 种方案\n", n, res)
+}
+
+func TestMinCostClimbingStairsDPComp(t *testing.T) {
+	cost := []int{0, 1, 10, 1, 1, 1, 10, 1, 1, 10, 1}
+	fmt.Printf("输入楼梯的代价列表为 %v\n", cost)
+
+	res := minCostClimbingStairsDP(cost)
+	fmt.Printf("爬完楼梯的最低代价为 %d\n", res)
+
+	res = minCostClimbingStairsDPComp(cost)
+	fmt.Printf("爬完楼梯的最低代价为 %d\n", res)
+}
diff --git a/codes/go/chapter_dynamic_programming/coin_change.go b/codes/go/chapter_dynamic_programming/coin_change.go
new file mode 100644
index 000000000..e2b0c6e76
--- /dev/null
+++ b/codes/go/chapter_dynamic_programming/coin_change.go
@@ -0,0 +1,66 @@
+// File: coin_change.go
+// Created Time: 2023-07-23
+// Author: Reanon (793584285@qq.com)
+
+package chapter_dynamic_programming
+
+import "math"
+
+/* 零钱兑换：动态规划 */
+func coinChangeDP(coins []int, amt int) int {
+	n := len(coins)
+	max := amt + 1
+	// 初始化 dp 表
+	dp := make([][]int, n+1)
+	for i := 0; i <= n; i++ {
+		dp[i] = make([]int, amt+1)
+	}
+	// 状态转移：首行首列
+	for a := 1; a <= amt; a++ {
+		dp[0][a] = max
+	}
+	// 状态转移：其余行列
+	for i := 1; i <= n; i++ {
+		for a := 1; a <= amt; a++ {
+			if coins[i-1] > a {
+				// 若超过背包容量，则不选硬币 i
+				dp[i][a] = dp[i-1][a]
+			} else {
+				// 不选和选硬币 i 这两种方案的较小值
+				dp[i][a] = int(math.Min(float64(dp[i-1][a]), float64(dp[i][a-coins[i-1]]+1)))
+			}
+		}
+	}
+	if dp[n][amt] != max {
+		return dp[n][amt]
+	}
+	return -1
+}
+
+/* 零钱兑换：动态规划 */
+func coinChangeDPComp(coins []int, amt int) int {
+	n := len(coins)
+	max := amt + 1
+	// 初始化 dp 表
+	dp := make([]int, amt+1)
+	for i := 1; i <= amt; i++ {
+		dp[i] = max
+	}
+	// 状态转移
+	for i := 1; i <= n; i++ {
+		// 倒序遍历
+		for a := 1; a <= amt; a++ {
+			if coins[i-1] > a {
+				// 若超过背包容量，则不选硬币 i
+				dp[a] = dp[a]
+			} else {
+				// 不选和选硬币 i 这两种方案的较小值
+				dp[a] = int(math.Min(float64(dp[a]), float64(dp[a-coins[i-1]]+1)))
+			}
+		}
+	}
+	if dp[amt] != max {
+		return dp[amt]
+	}
+	return -1
+}
diff --git a/codes/go/chapter_dynamic_programming/coin_change_ii.go b/codes/go/chapter_dynamic_programming/coin_change_ii.go
new file mode 100644
index 000000000..b4bccd50e
--- /dev/null
+++ b/codes/go/chapter_dynamic_programming/coin_change_ii.go
@@ -0,0 +1,54 @@
+// File: coin_change_ii.go
+// Created Time: 2023-07-23
+// Author: Reanon (793584285@qq.com)
+
+package chapter_dynamic_programming
+
+/* 零钱兑换 II：动态规划 */
+func coinChangeIIDP(coins []int, amt int) int {
+	n := len(coins)
+	// 初始化 dp 表
+	dp := make([][]int, n+1)
+	for i := 0; i <= n; i++ {
+		dp[i] = make([]int, amt+1)
+	}
+	// 初始化首列
+	for i := 0; i <= n; i++ {
+		dp[i][0] = 1
+	}
+	// 状态转移：其余行列
+	for i := 1; i <= n; i++ {
+		for a := 1; a <= amt; a++ {
+			if coins[i-1] > a {
+				// 若超过背包容量，则不选硬币 i
+				dp[i][a] = dp[i-1][a]
+			} else {
+				// 不选和选硬币 i 这两种方案的较小值
+				dp[i][a] = dp[i-1][a] + dp[i][a-coins[i-1]]
+			}
+		}
+	}
+	return dp[n][amt]
+}
+
+/* 零钱兑换 II：状态压缩后的动态规划 */
+func coinChangeIIDPComp(coins []int, amt int) int {
+	n := len(coins)
+	// 初始化 dp 表
+	dp := make([]int, amt+1)
+	dp[0] = 1
+	// 状态转移
+	for i := 1; i <= n; i++ {
+		// 倒序遍历
+		for a := 1; a <= amt; a++ {
+			if coins[i-1] > a {
+				// 若超过背包容量，则不选硬币 i
+				dp[a] = dp[a]
+			} else {
+				// 不选和选硬币 i 这两种方案之和
+				dp[a] = dp[a] + dp[a-coins[i-1]]
+			}
+		}
+	}
+	return dp[amt]
+}
diff --git a/codes/go/chapter_dynamic_programming/coin_change_test.go b/codes/go/chapter_dynamic_programming/coin_change_test.go
new file mode 100644
index 000000000..81cbba9c7
--- /dev/null
+++ b/codes/go/chapter_dynamic_programming/coin_change_test.go
@@ -0,0 +1,23 @@
+// File: coin_change_test.go
+// Created Time: 2023-07-23
+// Author: Reanon (793584285@qq.com)
+
+package chapter_dynamic_programming
+
+import (
+	"fmt"
+	"testing"
+)
+
+func TestCoinChange(t *testing.T) {
+	coins := []int{1, 2, 5}
+	amt := 4
+
+	// 动态规划
+	res := coinChangeDP(coins, amt)
+	fmt.Printf("凑到目标金额所需的最少硬币数量为 %d\n", res)
+
+	// 状态压缩后的动态规划
+	res = coinChangeDPComp(coins, amt)
+	fmt.Printf("凑到目标金额所需的最少硬币数量为 %d\n", res)
+}
diff --git a/codes/go/chapter_dynamic_programming/edit_distance.go b/codes/go/chapter_dynamic_programming/edit_distance.go
new file mode 100644
index 000000000..b34f1b582
--- /dev/null
+++ b/codes/go/chapter_dynamic_programming/edit_distance.go
@@ -0,0 +1,129 @@
+// File: edit_distance.go
+// Created Time: 2023-07-23
+// Author: Reanon (793584285@qq.com)
+
+package chapter_dynamic_programming
+
+/* 编辑距离：暴力搜索 */
+func editDistanceDFS(s string, t string, i int, j int) int {
+	// 若 s 和 t 都为空，则返回 0
+	if i == 0 && j == 0 {
+		return 0
+	}
+	// 若 s 为空，则返回 t 长度
+	if i == 0 {
+		return j
+	}
+	// 若 t 为空，则返回 s 长度
+	if j == 0 {
+		return i
+	}
+	// 若两字符相等，则直接跳过此两字符
+	if s[i-1] == t[j-1] {
+		return editDistanceDFS(s, t, i-1, j-1)
+	}
+	// 最少编辑步数 = 插入、删除、替换这三种操作的最少编辑步数 + 1
+	insert := editDistanceDFS(s, t, i, j-1)
+	deleted := editDistanceDFS(s, t, i-1, j)
+	replace := editDistanceDFS(s, t, i-1, j-1)
+	// 返回最少编辑步数
+	return MinInt(MinInt(insert, deleted), replace) + 1
+}
+
+/* 编辑距离：记忆化搜索 */
+func editDistanceDFSMem(s string, t string, mem [][]int, i int, j int) int {
+	// 若 s 和 t 都为空，则返回 0
+	if i == 0 && j == 0 {
+		return 0
+	}
+	// 若 s 为空，则返回 t 长度
+	if i == 0 {
+		return j
+	}
+	// 若 t 为空，则返回 s 长度
+	if j == 0 {
+		return i
+	}
+	// 若已有记录，则直接返回之
+	if mem[i][j] != -1 {
+		return mem[i][j]
+	}
+	// 若两字符相等，则直接跳过此两字符
+	if s[i-1] == t[j-1] {
+		return editDistanceDFSMem(s, t, mem, i-1, j-1)
+	}
+	// 最少编辑步数 = 插入、删除、替换这三种操作的最少编辑步数 + 1
+	insert := editDistanceDFSMem(s, t, mem, i, j-1)
+	deleted := editDistanceDFSMem(s, t, mem, i-1, j)
+	replace := editDistanceDFSMem(s, t, mem, i-1, j-1)
+	// 记录并返回最少编辑步数
+	mem[i][j] = MinInt(MinInt(insert, deleted), replace) + 1
+	return mem[i][j]
+}
+
+/* 编辑距离：动态规划 */
+func editDistanceDP(s string, t string) int {
+	n := len(s)
+	m := len(t)
+	dp := make([][]int, n+1)
+	for i := 0; i <= n; i++ {
+		dp[i] = make([]int, m+1)
+	}
+	// 状态转移：首行首列
+	for i := 1; i <= n; i++ {
+		dp[i][0] = i
+	}
+	for j := 1; j <= m; j++ {
+		dp[0][j] = j
+	}
+	// 状态转移：其余行列
+	for i := 1; i <= n; i++ {
+		for j := 1; j <= m; j++ {
+			if s[i-1] == t[j-1] {
+				// 若两字符相等，则直接跳过此两字符
+				dp[i][j] = dp[i-1][j-1]
+			} else {
+				// 最少编辑步数 = 插入、删除、替换这三种操作的最少编辑步数 + 1
+				dp[i][j] = MinInt(MinInt(dp[i][j-1], dp[i-1][j]), dp[i-1][j-1]) + 1
+			}
+		}
+	}
+	return dp[n][m]
+}
+
+/* 编辑距离：状态压缩后的动态规划 */
+func editDistanceDPComp(s string, t string) int {
+	n := len(s)
+	m := len(t)
+	dp := make([]int, m+1)
+	// 状态转移：首行
+	for j := 1; j <= m; j++ {
+		dp[j] = j
+	}
+	// 状态转移：其余行
+	for i := 1; i <= n; i++ {
+		// 状态转移：首列
+		leftUp := dp[0] // 暂存 dp[i-1, j-1]
+		dp[0] = i
+		// 状态转移：其余列
+		for j := 1; j <= m; j++ {
+			temp := dp[j]
+			if s[i-1] == t[j-1] {
+				// 若两字符相等，则直接跳过此两字符
+				dp[j] = leftUp
+			} else {
+				// 最少编辑步数 = 插入、删除、替换这三种操作的最少编辑步数 + 1
+				dp[j] = MinInt(MinInt(dp[j-1], dp[j]), leftUp) + 1
+			}
+			leftUp = temp // 更新为下一轮的 dp[i-1, j-1]
+		}
+	}
+	return dp[m]
+}
+
+func MinInt(a, b int) int {
+	if a < b {
+		return a
+	}
+	return b
+}
diff --git a/codes/go/chapter_dynamic_programming/edit_distance_test.go b/codes/go/chapter_dynamic_programming/edit_distance_test.go
new file mode 100644
index 000000000..b75f2a10e
--- /dev/null
+++ b/codes/go/chapter_dynamic_programming/edit_distance_test.go
@@ -0,0 +1,40 @@
+// File: edit_distance_test.go
+// Created Time: 2023-07-23
+// Author: Reanon (793584285@qq.com)
+
+package chapter_dynamic_programming
+
+import (
+	"fmt"
+	"testing"
+)
+
+func TestEditDistanceDFS(test *testing.T) {
+	s := "bag"
+	t := "pack"
+	n := len(s)
+	m := len(t)
+
+	// 暴力搜索
+	res := editDistanceDFS(s, t, n, m)
+	fmt.Printf("将 %s 更改为 %s 最少需要编辑 %d 步\n", s, t, res)
+
+	// 记忆化搜索
+	mem := make([][]int, n+1)
+	for i := 0; i <= n; i++ {
+		mem[i] = make([]int, m+1)
+		for j := 0; j <= m; j++ {
+			mem[i][j] = -1
+		}
+	}
+	res = editDistanceDFSMem(s, t, mem, n, m)
+	fmt.Printf("将 %s 更改为 %s 最少需要编辑 %d 步\n", s, t, res)
+
+	// 动态规划
+	res = editDistanceDP(s, t)
+	fmt.Printf("将 %s 更改为 %s 最少需要编辑 %d 步\n", s, t, res)
+
+	// 状态压缩后的动态规划
+	res = editDistanceDPComp(s, t)
+	fmt.Printf("将 %s 更改为 %s 最少需要编辑 %d 步\n", s, t, res)
+}
diff --git a/codes/go/chapter_dynamic_programming/knapsack.go b/codes/go/chapter_dynamic_programming/knapsack.go
new file mode 100644
index 000000000..64fbf33c7
--- /dev/null
+++ b/codes/go/chapter_dynamic_programming/knapsack.go
@@ -0,0 +1,87 @@
+// File: knapsack.go
+// Created Time: 2023-07-23
+// Author: Reanon (793584285@qq.com)
+
+package chapter_dynamic_programming
+
+import "math"
+
+/* 0-1 背包：暴力搜索 */
+func knapsackDFS(wgt, val []int, i, c int) int {
+	// 若已选完所有物品或背包无容量，则返回价值 0
+	if i == 0 || c == 0 {
+		return 0
+	}
+	// 若超过背包容量，则只能不放入背包
+	if wgt[i-1] > c {
+		return knapsackDFS(wgt, val, i-1, c)
+	}
+	// 计算不放入和放入物品 i 的最大价值
+	no := knapsackDFS(wgt, val, i-1, c)
+	yes := knapsackDFS(wgt, val, i-1, c-wgt[i-1]) + val[i-1]
+	// 返回两种方案中价值更大的那一个
+	return int(math.Max(float64(no), float64(yes)))
+}
+
+/* 0-1 背包：记忆化搜索 */
+func knapsackDFSMem(wgt, val []int, mem [][]int, i, c int) int {
+	// 若已选完所有物品或背包无容量，则返回价值 0
+	if i == 0 || c == 0 {
+		return 0
+	}
+	// 若已有记录，则直接返回
+	if mem[i][c] != -1 {
+		return mem[i][c]
+	}
+	// 若超过背包容量，则只能不放入背包
+	if wgt[i-1] > c {
+		return knapsackDFSMem(wgt, val, mem, i-1, c)
+	}
+	// 计算不放入和放入物品 i 的最大价值
+	no := knapsackDFSMem(wgt, val, mem, i-1, c)
+	yes := knapsackDFSMem(wgt, val, mem, i-1, c-wgt[i-1]) + val[i-1]
+	// 返回两种方案中价值更大的那一个
+	mem[i][c] = int(math.Max(float64(no), float64(yes)))
+	return mem[i][c]
+}
+
+/* 0-1 背包：动态规划 */
+func knapsackDP(wgt, val []int, cap int) int {
+	n := len(wgt)
+	// 初始化 dp 表
+	dp := make([][]int, n+1)
+	for i := 0; i <= n; i++ {
+		dp[i] = make([]int, cap+1)
+	}
+	// 状态转移
+	for i := 1; i <= n; i++ {
+		for c := 1; c <= cap; c++ {
+			if wgt[i-1] > c {
+				// 若超过背包容量，则不选物品 i
+				dp[i][c] = dp[i-1][c]
+			} else {
+				// 不选和选物品 i 这两种方案的较大值
+				dp[i][c] = int(math.Max(float64(dp[i-1][c]), float64(dp[i-1][c-wgt[i-1]]+val[i-1])))
+			}
+		}
+	}
+	return dp[n][cap]
+}
+
+/* 0-1 背包：状态压缩后的动态规划 */
+func knapsackDPComp(wgt, val []int, cap int) int {
+	n := len(wgt)
+	// 初始化 dp 表
+	dp := make([]int, cap+1)
+	// 状态转移
+	for i := 1; i <= n; i++ {
+		// 倒序遍历
+		for c := cap; c >= 1; c-- {
+			if wgt[i-1] <= c {
+				// 不选和选物品 i 这两种方案的较大值
+				dp[c] = int(math.Max(float64(dp[c]), float64(dp[c-wgt[i-1]]+val[i-1])))
+			}
+		}
+	}
+	return dp[cap]
+}
diff --git a/codes/go/chapter_dynamic_programming/knapsack_test.go b/codes/go/chapter_dynamic_programming/knapsack_test.go
new file mode 100644
index 000000000..8d1d9deb5
--- /dev/null
+++ b/codes/go/chapter_dynamic_programming/knapsack_test.go
@@ -0,0 +1,54 @@
+// File: knapsack_test.go
+// Created Time: 2023-07-23
+// Author: Reanon (793584285@qq.com)
+
+package chapter_dynamic_programming
+
+import (
+	"fmt"
+	"testing"
+)
+
+func TestKnapsack(t *testing.T) {
+	wgt := []int{10, 20, 30, 40, 50}
+	val := []int{50, 120, 150, 210, 240}
+	c := 50
+	n := len(wgt)
+
+	// 暴力搜索
+	res := knapsackDFS(wgt, val, n, c)
+	fmt.Printf("不超过背包容量的最大物品价值为 %d\n", res)
+
+	// 记忆化搜索
+	mem := make([][]int, n+1)
+	for i := 0; i <= n; i++ {
+		mem[i] = make([]int, c+1)
+		for j := 0; j <= c; j++ {
+			mem[i][j] = -1
+		}
+	}
+	res = knapsackDFSMem(wgt, val, mem, n, c)
+	fmt.Printf("不超过背包容量的最大物品价值为 %d\n", res)
+
+	// 动态规划
+	res = knapsackDP(wgt, val, c)
+	fmt.Printf("不超过背包容量的最大物品价值为 %d\n", res)
+
+	// 状态压缩后的动态规划
+	res = knapsackDPComp(wgt, val, c)
+	fmt.Printf("不超过背包容量的最大物品价值为 %d\n", res)
+}
+
+func TestUnboundedKnapsack(t *testing.T) {
+	wgt := []int{1, 2, 3}
+	val := []int{5, 11, 15}
+	c := 4
+
+	// 动态规划
+	res := unboundedKnapsackDP(wgt, val, c)
+	fmt.Printf("不超过背包容量的最大物品价值为 %d\n", res)
+
+	// 状态压缩后的动态规划
+	res = unboundedKnapsackDPComp(wgt, val, c)
+	fmt.Printf("不超过背包容量的最大物品价值为 %d\n", res)
+}
diff --git a/codes/go/chapter_dynamic_programming/min_cost_climbing_stairs_dp.go b/codes/go/chapter_dynamic_programming/min_cost_climbing_stairs_dp.go
new file mode 100644
index 000000000..b7c597eb3
--- /dev/null
+++ b/codes/go/chapter_dynamic_programming/min_cost_climbing_stairs_dp.go
@@ -0,0 +1,42 @@
+// File: min_cost_climbing_stairs_dp.go
+// Created Time: 2023-07-23
+// Author: Reanon (793584285@qq.com)
+
+package chapter_dynamic_programming
+
+import "math"
+
+/* 爬楼梯最小代价：动态规划 */
+func minCostClimbingStairsDP(cost []int) int {
+	n := len(cost) - 1
+	if n == 1 || n == 2 {
+		return cost[n]
+	}
+	// 初始化 dp 表，用于存储子问题的解
+	dp := make([]int, n+1)
+	// 初始状态：预设最小子问题的解
+	dp[1] = cost[1]
+	dp[2] = cost[2]
+	// 状态转移：从较小子问题逐步求解较大子问题
+	for i := 3; i <= n; i++ {
+		dp[i] = int(math.Min(float64(dp[i-1]), float64(dp[i-2]+cost[i])))
+	}
+	return dp[n]
+}
+
+/* 爬楼梯最小代价：状态压缩后的动态规划 */
+func minCostClimbingStairsDPComp(cost []int) int {
+	n := len(cost) - 1
+	if n == 1 || n == 2 {
+		return cost[n]
+	}
+	// 初始状态：预设最小子问题的解
+	a, b := cost[1], cost[2]
+	// 状态转移：从较小子问题逐步求解较大子问题
+	for i := 3; i <= n; i++ {
+		tmp := b
+		b = int(math.Min(float64(a), float64(tmp+cost[i])))
+		a = tmp
+	}
+	return b
+}
diff --git a/codes/go/chapter_dynamic_programming/min_path_sum.go b/codes/go/chapter_dynamic_programming/min_path_sum.go
new file mode 100644
index 000000000..366cba856
--- /dev/null
+++ b/codes/go/chapter_dynamic_programming/min_path_sum.go
@@ -0,0 +1,94 @@
+// File: min_path_sum.go
+// Created Time: 2023-07-23
+// Author: Reanon (793584285@qq.com)
+
+package chapter_dynamic_programming
+
+import "math"
+
+/* 最小路径和：暴力搜索 */
+func minPathSumDFS(grid [][]int, i, j int) int {
+	// 若为左上角单元格，则终止搜索
+	if i == 0 && j == 0 {
+		return grid[0][0]
+	}
+	// 若行列索引越界，则返回 +∞ 代价
+	if i < 0 || j < 0 {
+		return math.MaxInt
+	}
+	// 计算从左上角到 (i-1, j) 和 (i, j-1) 的最小路径代价
+	left := minPathSumDFS(grid, i-1, j)
+	up := minPathSumDFS(grid, i, j-1)
+	// 返回从左上角到 (i, j) 的最小路径代价
+	return int(math.Min(float64(left), float64(up))) + grid[i][j]
+}
+
+/* 最小路径和：记忆化搜索 */
+func minPathSumDFSMem(grid, mem [][]int, i, j int) int {
+	// 若为左上角单元格，则终止搜索
+	if i == 0 && j == 0 {
+		return grid[0][0]
+	}
+	// 若行列索引越界，则返回 +∞ 代价
+	if i < 0 || j < 0 {
+		return math.MaxInt
+	}
+	// 若已有记录，则直接返回
+	if mem[i][j] != -1 {
+		return mem[i][j]
+	}
+	// 左边和上边单元格的最小路径代价
+	left := minPathSumDFSMem(grid, mem, i-1, j)
+	up := minPathSumDFSMem(grid, mem, i, j-1)
+	// 记录并返回左上角到 (i, j) 的最小路径代价
+	mem[i][j] = int(math.Min(float64(left), float64(up))) + grid[i][j]
+	return mem[i][j]
+}
+
+/* 最小路径和：动态规划 */
+func minPathSumDP(grid [][]int) int {
+	n, m := len(grid), len(grid[0])
+	// 初始化 dp 表
+	dp := make([][]int, n)
+	for i := 0; i < n; i++ {
+		dp[i] = make([]int, m)
+	}
+	dp[0][0] = grid[0][0]
+	// 状态转移：首行
+	for j := 1; j < m; j++ {
+		dp[0][j] = dp[0][j-1] + grid[0][j]
+	}
+	// 状态转移：首列
+	for i := 1; i < n; i++ {
+		dp[i][0] = dp[i-1][0] + grid[i][0]
+	}
+	// 状态转移：其余行列
+	for i := 1; i < n; i++ {
+		for j := 1; j < m; j++ {
+			dp[i][j] = int(math.Min(float64(dp[i][j-1]), float64(dp[i-1][j]))) + grid[i][j]
+		}
+	}
+	return dp[n-1][m-1]
+}
+
+/* 最小路径和：状态压缩后的动态规划 */
+func minPathSumDPComp(grid [][]int) int {
+	n, m := len(grid), len(grid[0])
+	// 初始化 dp 表
+	dp := make([]int, m)
+	// 状态转移：首行
+	dp[0] = grid[0][0]
+	for j := 1; j < m; j++ {
+		dp[j] = dp[j-1] + grid[0][j]
+	}
+	// 状态转移：其余行列
+	for i := 1; i < n; i++ {
+		// 状态转移：首列
+		dp[0] = dp[0] + grid[i][0]
+		// 状态转移：其余列
+		for j := 1; j < m; j++ {
+			dp[j] = int(math.Min(float64(dp[j-1]), float64(dp[j]))) + grid[i][j]
+		}
+	}
+	return dp[m-1]
+}
diff --git a/codes/go/chapter_dynamic_programming/min_path_sum_test.go b/codes/go/chapter_dynamic_programming/min_path_sum_test.go
new file mode 100644
index 000000000..c4d43ef04
--- /dev/null
+++ b/codes/go/chapter_dynamic_programming/min_path_sum_test.go
@@ -0,0 +1,43 @@
+// File: min_path_sum_test.go
+// Created Time: 2023-07-23
+// Author: Reanon (793584285@qq.com)
+
+package chapter_dynamic_programming
+
+import (
+	"fmt"
+	"testing"
+)
+
+func TestMinPathSum(t *testing.T) {
+	grid := [][]int{
+		{1, 3, 1, 5},
+		{2, 2, 4, 2},
+		{5, 3, 2, 1},
+		{4, 3, 5, 2},
+	}
+	n, m := len(grid), len(grid[0])
+
+	// 暴力搜索
+	res := minPathSumDFS(grid, n-1, m-1)
+	fmt.Printf("从左上角到右下角的做小路径和为  %d\n", res)
+
+	// 记忆化搜索
+	mem := make([][]int, n)
+	for i := 0; i < n; i++ {
+		mem[i] = make([]int, m)
+		for j := 0; j < m; j++ {
+			mem[i][j] = -1
+		}
+	}
+	res = minPathSumDFSMem(grid, mem, n-1, m-1)
+	fmt.Printf("从左上角到右下角的做小路径和为  %d\n", res)
+
+	// 动态规划
+	res = minPathSumDP(grid)
+	fmt.Printf("从左上角到右下角的做小路径和为  %d\n", res)
+
+	// 状态压缩后的动态规划
+	res = minPathSumDPComp(grid)
+	fmt.Printf("从左上角到右下角的做小路径和为  %d\n", res)
+}
diff --git a/codes/go/chapter_dynamic_programming/unbounded_knapsack.go b/codes/go/chapter_dynamic_programming/unbounded_knapsack.go
new file mode 100644
index 000000000..6600f334e
--- /dev/null
+++ b/codes/go/chapter_dynamic_programming/unbounded_knapsack.go
@@ -0,0 +1,50 @@
+// File: unbounded_knapsack.go
+// Created Time: 2023-07-23
+// Author: Reanon (793584285@qq.com)
+
+package chapter_dynamic_programming
+
+import "math"
+
+/* 完全背包：动态规划 */
+func unboundedKnapsackDP(wgt, val []int, cap int) int {
+	n := len(wgt)
+	// 初始化 dp 表
+	dp := make([][]int, n+1)
+	for i := 0; i <= n; i++ {
+		dp[i] = make([]int, cap+1)
+	}
+	// 状态转移
+	for i := 1; i <= n; i++ {
+		for c := 1; c <= cap; c++ {
+			if wgt[i-1] > c {
+				// 若超过背包容量，则不选物品 i
+				dp[i][c] = dp[i-1][c]
+			} else {
+				// 不选和选物品 i 这两种方案的较大值
+				dp[i][c] = int(math.Max(float64(dp[i-1][c]), float64(dp[i][c-wgt[i-1]]+val[i-1])))
+			}
+		}
+	}
+	return dp[n][cap]
+}
+
+/* 完全背包：状态压缩后的动态规划 */
+func unboundedKnapsackDPComp(wgt, val []int, cap int) int {
+	n := len(wgt)
+	// 初始化 dp 表
+	dp := make([]int, cap+1)
+	// 状态转移
+	for i := 1; i <= n; i++ {
+		for c := 1; c <= cap; c++ {
+			if wgt[i-1] > c {
+				// 若超过背包容量，则不选物品 i
+				dp[c] = dp[c]
+			} else {
+				// 不选和选物品 i 这两种方案的较大值
+				dp[c] = int(math.Max(float64(dp[c]), float64(dp[c-wgt[i-1]]+val[i-1])))
+			}
+		}
+	}
+	return dp[cap]
+}