To address the limitations of the existing social bot detection methods in multimodal feature modeling， disguised behavior recognition， and generalization under weakly supervised scenarios， a social bot detection framework fusing multi-scale wavelet enhancement and self-supervised learning， named W2A-BotNet （Wavelet-to-Attention Bot Network）， was proposed. In the framework， a unified three-channel representation of textual semantics， user attributes， and social relations was constructed to alleviate modality conflicts； a Multi-scale Attention Wavelet Neural Operator Block （MAWNOBlock） was designed to perform time-frequency decomposition of behavioral sequences， thereby capturing both periodic patterns and abrupt anomalies； a multi-source collaborative fusion mechanism was introduced to achieve dynamic semantic alignment through cross-modal interactions and gating； a self-supervised pretraining based on follower count distribution was incorporated to enhance feature representation under limited labeled data. Experimental results show that the accuracy of the W2A-BotNet is improved by 0.35， 4.86， and 2.21 percentage points respectively compared to the suboptimal methods on Cresci-15， Cresci-17， and TwiBot-20 datasets， respectively. It can be seen that W2A-BotNet enhances the identification of bot accounts on social platforms effectively and provides a generalized detection framework for social network security governance.