In response to the issue of the inability of SINS / GNSS integrated navigation system to continuously correct errors in the event of a global navigation satellite system signal interruption, a dual-channel Residual-LSTM based SINS / GNSS integrated navigation algorithm is proposed. First, considering the nonlinear correlation difference between the input and output information of the model caused by the different transmission characteristics of SINS longitude and latitude errors, a dual-channel long and short-term memory neural network model structure with different weight coefficients was constructed. A adaptive forgetting information sharing mechanism was introduced to effectively use historical navigation data to fit and predict the longitude and latitude information. Second, in view of the model degradation and gradient vanishing problems existing in deep neural networks, a Residual-LSTM model structure is formed by establishing a Residual-LSTM high-speed channel between multi-layer and dual-channel LSTM networks to increase the information propagation paths between different network layers. Finally, the effectiveness of the proposed algorithm is verified by the real ship data. The experimental results show that compared with the SINS / GNSS integrated navigation algorithm based on conventional intelligence method, the proposed integrated navigation algorithm reduces the longitude error by 51. 97% and latitude error by 31. 45% during the GNSS signal interruption period. Keywords:SINS / GNSS integrated