Basic linear algebra subprogram (BLAS) is one of the most basic and important math libraries. The matrix-matrix operations covered in the level-3 BLAS functions are particularly significant for a standard BLAS library and are widely employed in many large-scale scientific and engineering computing applications. Additionally, level-3 BLAS functions are computing intensive functions and play a vital role in fully exploiting the computing performance of processors. Multi-core parallel optimization technologies are studied for level-3 BLAS functions on SW26010-Pro, a domestic processor. According to the memory hierarchy of SW26010-Pro, this study designs a multi-level blocking algorithm to exploit the parallelism of matrix operations. Then, a data-sharing scheme based on remote memory access (RMA) mechanism is proposed to improve the data transmission efficiency among CPEs. Additionally, it employs triple buffering and parameter tuning to fully optimize the algorithm and hide the memory access costs of direct memory access (DMA) and the communication overhead of RMA. Besides, the study adopts two hardware pipelines and several vectorized arithmetic/memory access instructions of SW26010-Pro and improves the floating-point computing efficiency of level-3 BLAS functions by writing assembly code manually for matrix-matrix multiplication, matrix equation solving, and matrix transposition. The experimental results show that level-3 BLAS functions can significantly improve the performance on SW26010-Pro by leveraging the proposed parallel optimization. The floating-point computing efficiency of single-core level-3 BLAS is up to 92% of the peak performance, while that of multi-core level-3 BLAS is up to 88% of the peak performance.