In recent years, deep learning has made a breakthrough in image recognition research, which has led to the rapid development of target detection technology. It can enhance visitors’' experience and improve the effect of science popularization by using target detection technology to develop aquarium fish target detection appAPP. In this paper, for 80 species of fish photographed at the aquarium, we usedfirstly, labelimge LabelImg software was used to mark the target of 80 kinds of fishes photographed in aquarium. A, and the marked target was exported to generate tfrecord data. Secondly, ssd_mobilenet_v1MobileNetV1 model was selected model for data training, and we obtained the fish target detection model was obtained through 200 000 times of iterative training. Finally, Two 2 interfaces and 12 classes were defined with TensorFlow multi-targets detection API call model, and Android system mobile APP was developed. After testing 1 620 pictures of 80 species of fish were tested, and, the accuracy rate was 92.59%. The program used tensorflow multi-target detection API to call the model. The average target detection time target was 40ms on Huawei MHA-AL00. Fish target detection app APP can realize fast fish recognition in aquarium and real-time detection of multiple fish targets. It ,can improve visitors’' visiting experience and help contribute to quantitative evaluation of science popularization.