Abstract: In order to effectively improve the intelligent management level of river and lake sand dredgers, an improved FaceNet based “ship face” recognition algorithm for river and lake sand dredgers was proposed.Firstly, a CA attention module was introduced behind the global average pooling layer of the FaceNet algorithm network to enhance the adaptive attention ability for regions of interest.Secondly, a linear layer was introduced at the end of the network during training to construct an individual “ship face” recognizer for sand dredgers.The combination of classification and recognition methods was applied to the “ship face” recognition of sand dredgers.Finally, Cross entropy Loss function was introduced into the training to assist the Triplet loss function in the original FaceNet algorithm to converge together.The experimental results showed that the accuracy of the improved FaceNet algorithm for identifying individual “ship face” objects on sand dredgers in daytime had increased by4.77percentage points compared to that before the improvement, reaching79.22%.The accuracy of identifying individual “ship face” objects of sand dredgers in night had increased by2.83percentage points.This algorithm is suitable for the “ship face” recognition task of sand dredgers and can provide effective technical support for the intelligent supervision of river and lake sand dredgers.