In view of the problems of scattered data sources,insufficient real-time performance and lack of interactivity in the surface flow pattern monitoring of the traditional hub channel,which can not meet the needs of real-time monitoring of the channel,this paper studies the panoramic intelligent digital enhancement technology of the surface flow pattern of the hub channel,focusing on the construction of multi-source heterogeneous data fusion method and the development of demonstration and verification system.A multi-source heterogeneous data fusion method based on deep learning is adopted,and an intelligent digital enhancement demonstration system based on B/S architecture is developed.It is concluded that the multi-source heterogeneous data fusion method effectively solves the core problem of multi-source data,the accuracy of space-time synchronization is significantly improved,the redundant data elimination rate is 35%-40%,and the data processing efficiency is more than 75% higher than the traditional method.The root-mean-square error of the fusion algorithm is only 0.03 m/s,and the accuracy is 83.3% higher than that of the traditional arithmetic average method.The system supports real-time processing of 200 sets of data per second and runs stably.The proposed multi-source heterogeneous data fusion method and the developed intelligent digital enhancement system realize the unified management,efficient processing and high-precision visualization of the channel surface flow state data,and provide a complete technical scheme and practical support for the intelligent analysis and real-time monitoring of the surface flow state.