Spectrum sensing is one of the key technologies to alleviate spectrum resource shortages, and intelligent spectrum sensing has become a hot research direction. To address the issues of insufficient feature extraction in existing spectrum sensing methods and poor sensing performance under low signal-to-noise (SNR) ratio conditions, a hybrid spectrum sensing model is proposed. The model consists of an Inception module, bidirectional gated recurrent unit, temporal attention mechanism, and fully connected layer network. Firstly, the Inception module extracts multi-scale spatial features from the received I/Q signals. Then, the bidirectional gated recurrent unit is used to capture the temporal sequence features of the signals, while the temporal attention mechanism enhances important temporal features. Finally, the fully connected layer network maps the extracted features to the classification space of spectrum states to complete classification and recognition. The experimental results show that the proposed method significantly improves perception performance compared to several existing spectrum sensing methods. The overall detection accuracy of the model reaches 84.55%, and when the SNR is -20 dB, the perception error of the method is 24%. The proposed method also demonstrates good adaptability to various modulation types of radio signals. It does not rely on any prior information and exhibits strong robustness in low SNR and complex radio environments. This approach achieves an effective balance between perception performance and model complexity, providing a new solution for intelligent spectrum sensing.