Computer-aided detection tools can help doctors reduce the situations of missed and false detections in clinical examinations to improve diagnostic accuracy and reduce the labor intensity of doctors. Aiming at the problem of the localization and identification of submucosal tumors in ultrasound gastroscopy, a submucosal tumor detection algorithm combined with multi-scale feature and subspace attention (MFSA-YOLOv7t) was proposed. Firstly, the small target prediction head was removed to make the network lightweight while maintaining accuracy. Secondly, A multi-scale feature fusion module was proposed based on shallow features to aggregate details. Then, the algorithm improved the up-sampling structure to retain the upper layer information to the greatest extent and enhance the feature perception field. Finally, the coordinate subspace attention module was introduced to capture tumor location and boundary features to improve the detection performance of submucosal tumors. Experiments show that the mAP, sensitivity, and accuracy of MFSA-YOLOv7t reach 97.32%, 96.99%, and 96.24%, respectively, compared with YOLOv7-tiny, MFSA-YOLOv7t has a great improvement in detection performance, which is improved by 2.39%, 2.75%, and 2.59%, respectively. MFSA-YOLOv7t provides a more reliable reference of tumor type for doctors in clinical examination of the auxiliary diagnosis and also provides a new idea and a research direction for detecting submucosal tumors.