diff --git a/6 b/6
new file mode 100644
index 0000000..167b45a
--- /dev/null
+++ b/6
@@ -0,0 +1,129 @@
+#include <stdio.h>
+#include <stdlib.h>
+#include <ctime>
+
+#include <arm_neon.h>
+
+#define ROWS 1024  
+#define COLS 1024  
+
+
+void sparseToDense(float* values, int* rowIndex, int* colIndex, int nonZeroCount, float denseMatrix[ROWS][COLS]) {
+    
+    for (int i = 0; i < ROWS; i++) {
+        for (int j = 0; j < COLS; j++) {
+            denseMatrix[i][j] = 0;
+        }
+    }
+
+    
+    for (int k = 0; k < nonZeroCount; k++) {
+        int row = rowIndex[k];
+        int col = colIndex[k];
+        float value = values[k];
+        denseMatrix[row][col] = value;
+    }
+}
+
+
+void matmul_optimized(float** A, float** B, float** C, int n) {
+    for (int i = 0; i < n; i++) {
+        for (int j = 0; j < n; j++) {
+            float32x4_t vecC = vdupq_n_f32(0); 
+            for (int k = 0; k < n; k += 4) {
+                
+                float32x4_t vecA = vld1q_f32(&A[i][k]);
+                
+                float32x4_t vecB = vld1q_f32(&B[k][j]);
+
+                
+                vecC = vmlaq_f32(vecC, vecA, vecB);
+            }
+            
+            C[i][j] = vgetq_lane_f32(vecC, 0) + vgetq_lane_f32(vecC, 1) +
+                      vgetq_lane_f32(vecC, 2) + vgetq_lane_f32(vecC, 3);
+        }
+    }
+}
+
+int main() {
+
+    float A_values[] = {1, 2, 3};
+    int A_rowIndex[] = {0, 1, 2};
+    int A_colIndex[] = {0, 1, 2};
+    int A_nonZeroCount = 3;
+
+
+    float B_values[] = {4, 5, 6};
+    int B_rowIndex[] = {0, 1, 2};
+    int B_colIndex[] = {0, 1, 2};
+    int B_nonZeroCount = 3;
+
+
+    float denseMatrixA[ROWS][COLS];
+
+    float denseMatrixB[ROWS][COLS];
+
+    float resultMatrix[ROWS][COLS];
+
+
+    sparseToDense(A_values, A_rowIndex, A_colIndex, A_nonZeroCount, denseMatrixA);
+    
+    printf("常规矩阵A：\n");
+    for (int i = 0; i < ROWS; i++) {
+        for (int j = 0; j < COLS; j++) {
+            printf("%f ", denseMatrixA[i][j]);
+        }
+        printf("\n");
+    }
+
+
+    sparseToDense(B_values, B_rowIndex, B_colIndex, B_nonZeroCount, denseMatrixB);
+   
+    printf("常规矩阵B：\n");
+    for (int i = 0; i < ROWS; i++) {
+        for (int j = 0; j < COLS; j++) {
+            printf("%f ", denseMatrixB[i][j]);
+        }
+        printf("\n");
+    }
+
+
+    float* matrixAPtr[ROWS];
+    for (int i = 0; i < ROWS; i++) {
+        matrixAPtr[i] = denseMatrixA[i];
+    }
+    float* matrixBPtr[ROWS];
+    for (int i = 0; i < ROWS; i++) {
+        matrixBPtr[i] = denseMatrixB[i];
+    }
+    float* resultMatrixPtr[ROWS];
+    for (int i = 0; i < ROWS; i++) {
+        resultMatrixPtr[i] = resultMatrix[i];
+    }
+
+    clock_t start_time, end_time;
+
+    start_time = clock();
+
+ 
+    matmul_optimized((float**)matrixAPtr, (float**)matrixBPtr, (float**)resultMatrixPtr, ROWS);
+
+
+    end_time = clock();
+
+
+    double elapsed_time = ((double)(end_time - start_time)) / CLOCKS_PER_SEC;
+    printf("优化的稀疏矩阵乘法（使用NEON）的运行时间：%f 秒\n", elapsed_time);
+
+
+    printf("结果矩阵：\n");
+    for (int i = 0; i < ROWS; i++) {
+        for (int j = 0; j < COLS; j++) {
+            printf("%f ", resultMatrix[i][j]);
+        }
+        printf("\n");
+    }
+
+    return 0;
+}
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