Exploring and protecting fish is an important part of maintaining the balance of the marine ecological environment. However, the complex underwater environment affected by light, water quality, and occlusions makes it difficult to identify blurred fish images captured underwater and consequently restricts the speed and accuracy of underwater fish target detection. To solve the above problem, this study proposes a marine fish identification model based on improved fully convolutional one-stage object detection (FCOS). Specifically, the model takes the one-stage FCOS algorithm as the basic structure and uses the lightweight MobileNetv2 as the backbone network, which not only ensures the detection accuracy but also improves the detection; then, an adaptive spatial feature fusion (ASFF) module is introduced to avoid the inconsistency in scale features and improve detection accuracy; finally, the center-ness branch is introduced into the regression branch, and the generalized intersection over union (GIoU) loss is introduced to improve detection performance. Regarding the experimental dataset, the pictures in the public dataset Fish4Knowledge (F4K) and video frame screenshots are utilized, and the model with the optimal training performance is selected for evaluation. The results show that the average detection accuracy of the proposed new model on the above datasets is 99.79% and 99.88%, respectively. Compared with the original model and other detection models, the proposed model provides higher detection accuracy and identification speed. The model in this study can provide a reference for marine fish identification.