SIMD extension is an acceleration component integrated into the general processor, aiming at exploiting data level parallelism in multimedia and scientific computation programs. Two of the mainstream vectorization methods are loop-based method oriented to inter-iteration and SLP method oriented to intra-iteration. Derived from SLP, loop-aware method transforms inter-iteration to intra-iteration through loop unrolling, so as to obtain enough isomorphic statements and then uses SLP to explore vectorization. However, when loop unrolling is illegal or SIMD parallelism is lower than the vector factor, loop-aware method cannot exploit SIMD parallelism of programs. To address this drawback, a vectorization method guided by SIMD parallelism for loops is proposed. Alternative scheme for loop vectorization is constructed in view of inter-iteration parallelism, intra-iteration parallelism and vector factor. Simultaneously, insufficient vectorization is proposed to vectorize loops whose parallelism is lower than the vector factor. Lastly, vectorized loop is unrolled according to SIMD parallelism. Test results by benchmarks show that vectorization method guided by SIMD parallelism outperforms loop-aware method by 107.5%. Moreover, the performance is improved by 12.1% compared with loop-aware method.