For SLP (Superword Level Parallelism) algorithm cannot effectively process the large-scale applications covered with few parallel codes, and the codes which can be vectorized may be adverse to vectorization. A new improved algorithm for SLP was proposed, namely NSLPO. First of all, the non-isomorphic statements which cannot be vectorized were transformed to isomorphic statements as far as possible, thus locating the opportunities of vectorization which SLP has lost. Secondly, the Max Common Subgraph (MCS) was built by adding redundant nodes, and the supplement diagram of SLP was got by using some optimization such as redundancy deleting, which can greatly increase the parallelism of program. At last, the codes which are harmful to vectorization were exclued out of vectorization by using cutting method and executed in serial, only the valuable codes for vectorization were vectorized to improve the efficiency of programs as far as possible. Experiments were conducted on widely used kernel test sets. The experimental results show that compared with the SLP algorithm, the proposed NSLPO algorithm has better performance and its running time was reduced by 9.1%.