In the process of tuna seiners seeking tuna schools, it incurs high cost of fuel, manpower and material resources. Currently, it has become a common concern to improve fish searching efficiency and reduce the fish catches cost. In this study, computer video intelligent aided analysis and recognition program based on YOLOv3 model was used to realize the automatic identification of tuna shoal characteristics, thus to reduce the time of finding fish and improve fishing efficiency. A characteristic data set of tuna shoal was constructed by pre-processing the characteristic video provided by the Shanghai Kaichuang Deep Sea Fishing Co. LTD and the data set was trained for recognition. The trained model was deployed on a computer with an inference task for simulation identification of tuna shoal. The results show that the recognition accuracy of the simulation was 68.6%. The study proves that the YOLOv3 based characteristic recognition model has a practical application value in the forecast of tuna fishing conditions, and the study results could be referred to for future tuna shoal features recognition.