Nowadays, more and more processors are integrated with SIMD (single instruction multiple data) extensions, and most of the compilers have applied automatic vectorization, but the vectorization usually targets the innermost loop, there have been no easy vectorization approaches that deal with the loop nest. This paper brings out an automatic vectorization approach to vectorize nested loops form outer to inner. The paper first analyzes whether the loop can do direct unroll-and-jam through dependency analysis. Next, this study collects the values about the loop that will influence vectorization performance, including whether it can do direct unroll-and-jam, the number of array references that are continuous for this loop index and the loop region. Moreover, the study also presents an aggressive algorithm that will be used to decide which loops need to do unroll-and-jam at last generate SIMD code using SLP (superword level parallelism) algorithm. The test results on Intel platform show that the average speedup factor of some numerical/video/communication kernels achieved by this approach is 2.13/1.41, better than the innermost loop vectorization and simple outer-loop vectorization, the speedup factor of some common kernels can reach 5.3.