Blind decoding is the core technique to recover the original data sequence without any prior information in non-cooperative communication scenarios. However， the absence of prior information poses severe challenges to blind decoding， making the integrated implementation of blind recognition of channel coding scheme and blind decoding a critical and urgent problem to be solved in this field. In current wireless communication systems， limited and fixed standard protocols are widely adopted， and specific protocols have a strong binding relationship with their corresponding channel coding schemes. Therefore， the coding scheme adopted by the communication system can be inferred through the blind recognition of the communication protocol. To address the above challenges， this paper proposes a novel hybrid deep learning network model， and constructs an integrated solution for blind recognition of channel coding and blind decoding. Numerical experimental results demonstrate that， under low signal-to-noise ratio regimes， the proposed model achieves higher recognition accuracy of channel coding and stronger anti-noise robustness compared with conventional schemes. This work provides a new technical approach for the research of blind decoding technology， and also supplies theoretical and experimental support for engineering applications in the field of information countermeasures.