The invention provides a ship target detection method based on joint training of deep learning features and visual features, which includes the following steps: sample data collection, CNN feature extraction, traditional moment invariant feature and LOMO feature extraction, feature dimension reduction, feature fusion network FCNN construction, and training the network with sample data and testingthe model with test data. Compared with the prior art, the visual feature extraction process of the invention comprehensively considers the characteristics of the ship shape, color and texture, so that the detection process is interpretable, and other features other than the traditional features can be learned in the normalized CNN back propagation process. This method is fast and efficient, highaccuracy, for complex scenes such as clouds and fog, cloudy days, rain and other circumstances still have good detection results, high robustness. Features complementary to the traditional features can be extracted, and the speed is very fast, can achieve the effect of real-time monitoring.