diff --git a/solution/1500-1599/1570.Dot Product of Two Sparse Vectors/README.md b/solution/1500-1599/1570.Dot Product of Two Sparse Vectors/README.md
index 2a47af11bbc73..ba340420a0b91 100644
--- a/solution/1500-1599/1570.Dot Product of Two Sparse Vectors/README.md	
+++ b/solution/1500-1599/1570.Dot Product of Two Sparse Vectors/README.md	
@@ -77,7 +77,7 @@ v1.dotProduct(v2) = 0*0 + 1*0 + 0*0 + 0*0 + 0*2 = 0
 
 ### 方法一：哈希表
 
-我们用哈希表 $d$ 来存储非零元素，其中键为下标，值为对应的值。我们遍历 $nums$，如果 $nums[i]$ 不为 $0$，我们就将 $(i, nums[i])$ 加入到哈希表 $d$ 中。
+我们用哈希表 $d$ 来存储非零元素，其中键为下标，值为对应的值。我们遍历 $\textit{nums}$，如果 $\textit{nums}[i]$ 不为 $0$，我们就将 $(i, \textit{nums}[i])$ 加入到哈希表 $d$ 中。
 
 在计算点积时，我们遍历非零元素较少的哈希表，并判断另一个哈希表中是否存在对应的键，如果存在就将对应的值相乘并累加到答案中。
 
@@ -261,6 +261,46 @@ class SparseVector {
  */
 ```
 
+#### Rust
+
+```rust
+use std::collections::HashMap;
+
+#[derive(Clone)]
+struct SparseVector {
+    d: HashMap<usize, i32>,
+}
+
+impl SparseVector {
+    fn new(nums: Vec<i32>) -> Self {
+        let mut d = HashMap::new();
+        for (i, &x) in nums.iter().enumerate() {
+            if x != 0 {
+                d.insert(i, x);
+            }
+        }
+        SparseVector { d }
+    }
+
+    fn dot_product(&self, vec: SparseVector) -> i32 {
+        let (a, b) = (&self.d, &vec.d);
+        let mut ans = 0;
+
+        if a.len() > b.len() {
+            return vec.dot_product(self.clone());
+        }
+
+        for (&i, &x) in a.iter() {
+            if let Some(&y) = b.get(&i) {
+                ans += x * y;
+            }
+        }
+
+        ans
+    }
+}
+```
+
 <!-- tabs:end -->
 
 <!-- solution:end -->
diff --git a/solution/1500-1599/1570.Dot Product of Two Sparse Vectors/README_EN.md b/solution/1500-1599/1570.Dot Product of Two Sparse Vectors/README_EN.md
index 5aa2c99489ba6..b57560270588e 100644
--- a/solution/1500-1599/1570.Dot Product of Two Sparse Vectors/README_EN.md	
+++ b/solution/1500-1599/1570.Dot Product of Two Sparse Vectors/README_EN.md	
@@ -73,7 +73,13 @@ v1.dotProduct(v2) = 0*0 + 1*0 + 0*0 + 0*0 + 0*2 = 0
 
 <!-- solution:start -->
 
-### Solution 1
+### Solution 1: Hash Map
+
+We use a hash map $d$ to store non-zero elements, where the key is the index, and the value is the corresponding value. We iterate through $\textit{nums}$, and if $\textit{nums}[i]$ is not $0$, we add $(i, \textit{nums}[i])$ to the hash map $d$.
+
+When calculating the dot product, we iterate through the hash map with fewer non-zero elements and check if the other hash map contains the corresponding key. If it exists, we multiply the corresponding values and add them to the result.
+
+The time complexity is $O(n)$, and the space complexity is $O(n)$, where $n$ is the length of the array.
 
 <!-- tabs:start -->
 
@@ -253,6 +259,46 @@ class SparseVector {
  */
 ```
 
+#### Rust
+
+```rust
+use std::collections::HashMap;
+
+#[derive(Clone)]
+struct SparseVector {
+    d: HashMap<usize, i32>,
+}
+
+impl SparseVector {
+    fn new(nums: Vec<i32>) -> Self {
+        let mut d = HashMap::new();
+        for (i, &x) in nums.iter().enumerate() {
+            if x != 0 {
+                d.insert(i, x);
+            }
+        }
+        SparseVector { d }
+    }
+
+    fn dot_product(&self, vec: SparseVector) -> i32 {
+        let (a, b) = (&self.d, &vec.d);
+        let mut ans = 0;
+
+        if a.len() > b.len() {
+            return vec.dot_product(self.clone());
+        }
+
+        for (&i, &x) in a.iter() {
+            if let Some(&y) = b.get(&i) {
+                ans += x * y;
+            }
+        }
+
+        ans
+    }
+}
+```
+
 <!-- tabs:end -->
 
 <!-- solution:end -->
diff --git a/solution/1500-1599/1570.Dot Product of Two Sparse Vectors/Solution.rs b/solution/1500-1599/1570.Dot Product of Two Sparse Vectors/Solution.rs
new file mode 100644
index 0000000000000..31888739a54e6
--- /dev/null
+++ b/solution/1500-1599/1570.Dot Product of Two Sparse Vectors/Solution.rs	
@@ -0,0 +1,35 @@
+use std::collections::HashMap;
+
+#[derive(Clone)]
+struct SparseVector {
+    d: HashMap<usize, i32>,
+}
+
+impl SparseVector {
+    fn new(nums: Vec<i32>) -> Self {
+        let mut d = HashMap::new();
+        for (i, &x) in nums.iter().enumerate() {
+            if x != 0 {
+                d.insert(i, x);
+            }
+        }
+        SparseVector { d }
+    }
+
+    fn dot_product(&self, vec: SparseVector) -> i32 {
+        let (a, b) = (&self.d, &vec.d);
+        let mut ans = 0;
+
+        if a.len() > b.len() {
+            return vec.dot_product(self.clone());
+        }
+
+        for (&i, &x) in a.iter() {
+            if let Some(&y) = b.get(&i) {
+                ans += x * y;
+            }
+        }
+
+        ans
+    }
+}