With the rapid expansion of large data centers and storage systems, the prediction and diagnosis of disk failures has become a critical task to ensure system reliability and reduce downtime. In this study, we propose a novel deep learning model for hard disk failure prediction, which integrates multilayer convolutional blocks, a multi-head self-attention mechanism, and a hybrid TSMixer layer. The model first extracts multilevel features from hard disk operational data through multilayer convolution, and then focuses on critical time series information using the multi-head attention mechanism to capture long-term dependencies. Finally, the model is enhanced with a TSMixer layer to learn complex time series dynamics. Experimental results on publicly available datasets show that the proposed model outperforms existing methods in terms of prediction accuracy. This research offers an efficient and accurate solution for proactive disk failure management in data centers, facilitating the implementation of preventive maintenance strategies and improving overall system reliability.