Bearings are critical transmission components in rotating machinery whose operating conditions directly affect equipment safety and efficiency, making real-time monitoring and accurate prediction of remaining useful life (RUL) essential for preventing failures. Although deep learning-based self-attention models are widely used for life prediction, their reliance on feature embeddings and positional encoding hinders the capture of subtle degradation changes. Embedded Gaussian masks improve detection of delicate local degradation features, but their cubic computational complexity with data length limits practical efficiency. To overcome these issues, this study proposes a collaborative framework that integrates state-space model (SSM) with attention mechanisms. By incorporating wavelet transforms and cepstral filtering into the state-space process, the new feature tokenization module replaces traditional embeddings to enhance degradation representation. A gating-based dynamic selection algorithm then analyzes feature evolution, trend fluctuations, and noise resistance in real time to intelligently extract key degradation indicators, while a lightweight multi-scale attention module decodes life mapping by merging local vibration characteristics with global degradation patterns and reducing computational load. Comparative experiments on the PRONOSTIA dataset (conditions 1 and 2) and full-life test data from Jiangsu Lianyy Measurement and Control Technology Co., Ltd. show MAE improvements of 11.4%, 20%, and 15.4% and RMSE enhancements of 15.2%, 18.5%, and 27.4%, with ablation studies confirming up to a 55.6% boost in computational efficiency.