To address the challenges of small inter-class differences in pest details，severe field background interference，and imbalanced sample distribution，a complementary feature fusion dual-stream network for pest recognition is proposed.This network combines the local perception capability of convolutional neural networks with the global modeling ability of the Mamba model，capturing and integrating the global and local information of pest images.A hierarchical multiscale perception module is designed to extract multi-scale image features through grouped hierarchical convolution and enhance pest detail information with a detail enhancement perception strategy.An adaptive focusing Mamba module is designed to locate key pest regions using dynamic convolution operators and reduce complex background interference.Additionally，an attention-weighted fusion module is designed to achieve adaptive interaction and optimization of global and local features through a cross-attention mechanism，further improving the accuracy of semantic expression.A balanced loss function is constructed to mitigate the effects of class imbalance in the dataset.The experimental results show that the network achieves an accuracy of 71.19%on the large-scale pest dataset IP102，and an accuracy of 99.36%on the D0 dataset，demonstrating its ability to effectively identify pest species.