Currently, two vectorization methods which exploit Simple Instruction Multiple Data (SIMD) parallelism are loop-based method and Superword Level Parallel (SLP) method. Focusing on the problem that the current compiler cannot realize function level vectorization, a method of function level vectorization based on static single assignment was proposed. Firstly, the variable properties of program were analysed, and then a set of compiling directives including SIMD function annotations, uniform clauses, linear clauses were used to realize function level vectorization. Finally, the vectorized code was optimized by using the variable attribute result. Some test cases from the field of multimedia and image processing were selected to test the function and performance of the proposed function level vectorization on Sunway platform. Compared with the scalar program execution results, the execution of the program after the function level vectorization is more efficient. The experimental results show that the function level vectorization can achieve the same effect of task level parallelism, which is instructive to realize the automatic function level vectorization.