diff --git a/Chen/CHEN_6.c b/Chen/CHEN_6.c
new file mode 100644
index 0000000..ad94b51
--- /dev/null
+++ b/Chen/CHEN_6.c
@@ -0,0 +1,88 @@
+#include <arm_neon.h>
+#include <stdio.h>
+#include <time.h>
+#include <stdlib.h>
+#include <string.h>
+#define SIZE 1024
+#define DENSE_MATRIX_SIZE 5
+
+float** sparce_matmul_coo(float* A_values, int* A_rowIndex, int* A_colIndex, int A_nonZeroCount,
+                          float* B_values, int* B_rowIndex, int* B_colIndex, int B_nonZeroCount);
+void matmul_optimized(float** A, float** B, float** C, int n);
+void print_matrix(float** m, int rows, int cols);
+int main() {
+	// 矩阵 A 的 COO 格式
+	float A_values[] = {1, 2, 3, 4, 5}; 
+	int A_rowIndex[] = {0, 0, 1, 2, 2}; 
+	int A_colIndex[] = {0, 2, 1, 0, 2}; 
+	int A_nonZeroCount = 5;
+	// 矩阵 B 的 Coo 格式
+	float B_values[] = {6, 8, 7, 9}; 
+	int B_rowIndex[] = {0, 2, 1, 2}; 
+	int B_colIndex[] = {0, 0, 1, 2}; 
+	int B_nonZeroCount = 4;// 结果矩阵 C 的 Coo 格式
+	float C_values[SIZE]; 
+	int C_rowIndex[SIZE]; 
+	int C_colIndex[SIZE]; 
+	int C_nonZeroCount = 0;
+
+	clock_t start = clock();
+	float** ans = sparce_matmul_coo(A_values, A_rowIndex, A_colIndex, A_nonZeroCount,
+	                  B_values, B_rowIndex, B_colIndex, B_nonZeroCount);
+	clock_t end = clock();
+	printf("优化的稀疏矩阵乘法时间：%lf\n", (double)(end-start) / CLOCKS_PER_SEC);
+	print_matrix(ans, DENSE_MATRIX_SIZE, DENSE_MATRIX_SIZE);
+}
+
+float** sparce_matmul_coo(float* A_values, int* A_rowIndex, int* A_colIndex, int A_nonZeroCount,
+                          float* B_values, int* B_rowIndex, int* B_colIndex, int B_nonZeroCount) {
+	// 分配内存空间
+	float** A = (float**)malloc(sizeof(float*) * DENSE_MATRIX_SIZE);
+	float** B = (float**)malloc(sizeof(float*) * DENSE_MATRIX_SIZE);
+	float** C = (float**)malloc(sizeof(float*) * DENSE_MATRIX_SIZE);
+	int i;
+	for(i=0; i<DENSE_MATRIX_SIZE; i++) {
+		A[i] = (float*)malloc(sizeof(float) * DENSE_MATRIX_SIZE);
+		B[i] = (float*)malloc(sizeof(float) * DENSE_MATRIX_SIZE);
+		C[i] = (float*)malloc(sizeof(float) * DENSE_MATRIX_SIZE);
+		memset(A[i], 0, sizeof(float) * DENSE_MATRIX_SIZE);
+		memset(B[i], 0, sizeof(float) * DENSE_MATRIX_SIZE);
+		memset(C[i], 0, sizeof(float) * DENSE_MATRIX_SIZE);
+	}
+	for (i=0; i<A_nonZeroCount; i++) A[A_rowIndex[i]][A_colIndex[i]] = A_values[i];
+	for (i=0; i<B_nonZeroCount; i++) B[B_rowIndex[i]][B_colIndex[i]] = B_values[i];
+	matmul_optimized(A, B, C, DENSE_MATRIX_SIZE);
+
+	// 释放内存空间
+	for(i=0; i<DENSE_MATRIX_SIZE; i++) {
+		free(A[i]);
+		free(B[i]);
+	}
+	free(A); free(B);
+	return C;
+}
+
+void matmul_optimized(float** A, float** B, float** C, int n) {
+	int i, j, k;
+	float32x4_t vecA, vecB, vecC;
+	for(i=0; i<n; i++)
+	for(j=0; j<n; j++) {
+		vecC = vdupq_n_f32(0.0);
+		for(k=0; k<n; k+=4) {
+			vecA = vld1q_f32(&A[i][k]);
+			vecB = vld1q_f32(&A[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);
+	}
+}
+void print_matrix(float** m, int rows, int cols) {
+	int i, j;
+	for(i=0; i<rows; i++) {
+		for(j=0; j<cols; j++) {
+			printf("%5.0f ", m[i][j]);
+		}
+		printf("\n");
+	}
+}
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