With the popularization of multi-core processors, automatic parallelization of serial codes in embedded legacy systems is a research hotspot, while there are technical challenges in the automatic parallelization method for complex nested loops with imperfect nested structure and non-affine dependency characteristics. This study proposes an automatic parallelization framework (CNLPF) for complex nested loops based on LLVM Pass. Firstly, a representation model of complex nested loops, namely loop structure tree, is proposed, and the regular region of nested loops is automatically converted into a loop structure tree representation. Then, the data dependency analysis is carried out on the loop structure tree to construct intra-loop and inter-loop dependency relationship. Finally, the parallel loop program is generated based on the OpenMP shared memory programming model. For the 6 program cases in the SPEC2006 data set containing nearly 500 complex nested loops, the statistics of the proportion of complex nested loops and the parallel performance acceleration test were carried out respectively. The results show that the automatic parallelization framework proposed in this study can deal with complex nested loops that cannot be optimized by LLVM Polly, which enhances the parallel compilation and optimization capabilities of LLVM, and the method combined with Polly optimization improves the acceleration effect of Polly optimization alone by 9%-43%.