With the continuous development of bridge technology, the condition assessment of large bridges has gradually attracted attention. Structural Health Monitoring (SHM) technology provides valuable information about a structure's existing health, keeping it safe and uninterrupted use under various operating conditions by mitigating risks and hazards on time. At the same time, the problem of bridge underwater structure disease is becoming more obvious, affecting the safe operation of the bridge structure. It is necessary to test the bridge’s underwater structure. This paper develops a bridge underwater structure health monitoring system by combining building information modeling (BIM) and an underwater structure damage algorithm. This paper is verified by multiple image recognition networks, and compared with the advantages of different networks, the YOLOV4 network is used as the main body to improve, and a lightweight convolutional neural network (Lite-yolov4) is built. At the same time, the accuracy of disease identification and the performance of each network are tested in various experimental environments, and the reliability of the underwater structure detection link is verified.