BLAS (Basic Linear Algebra Subprograms) is a specification that prescribes a set of low-level routines for performing common linear algebra operations such as vector addition, scalar multiplication, dot products, linear combinations, and matrix multiplication. The functions in this library are divided into three levels, and each level provides basic operations between vector-vector (level 1), vector-matrix (level 2), and matrix-matrix (level 3), respectively. In this paper, we study the parallel implementation of BLAS level 1 and level 2 functions on Shenwei many-core processor, and make full use of the characteristics of the platform to optimize their performance, and sum up the parallel implementation and optimization techniques of the program on Shenwei platform. Shenwei 26010 CPU uses heterogeneous multi-core architecture, and has an obvious advantage in operating speed. Many computing cores provide large-scale parallel processing capabilities, so that, double precision floating-point computing performance of one single chip can reach 3TFLOPS. The experimental results show that the average speedup of BLAS level 1 and level 2 functions is as high as 11.x and 6.x times of GotoBLAS reference implementations respectively.