Student engagement is an essential indicator of educational quality and an instability behavior influenced by teachers. However, research on how teacher support influences this behavioral outcome in physical education has started late compared to academic settings. Hence, this systematic review aims to examine the empirical literature regarding the relationship between perceived teacher support and student engagement in physical education. This review complied with the PRISMA statement and ultimately found 11 eligible studies through the literature utilizing several databases, namely, Web of Science, SCOPUS, PsycINFO, ERIC, and SPORTDiscus. The results revealed a significant positive relationship between perceived teacher support and multiple dimensions of student engagement in physical education, especially in behavioral and emotional engagement. Mediating effects were found in five studies, and autonomous motivation and psychological need satisfaction were the crucial mediators. Teacher support plays a vital role in positive student–teacher interactions and students demonstrate enhanced engagement in physical education learning when teachers provide autonomy, competence, and emotional support. This study has great significance for developing instructional strategies to improve the sustainability of student engagement in physical education and also provides insight for researchers exploring options for optimizing motivational teaching styles to promote the development of sustainable teaching practices.

In order to realize the intelligence of underwater robots, this exploration proposes a submersible vision system based on neurorobotics to obtain the target information in underwater camera data. This exploration innovatively proposes a method based on the convolutional neural network (CNN) to mine the target information in underwater camera data. First, the underwater functions of the manned submersible are analyzed and mined to obtain the specific objects and features of the underwater camera information. Next, the dataset of the specific underwater target image is further constructed. The acquisition system of underwater camera information of manned submersibles is designed through the Single Shot-MultiBox Detector algorithm of deep learning. Furthermore, CNN is adopted to classify the underwater target images, which realizes the intelligent detection and classification of underwater targets. Finally, the model’s performance is tested through experiments, and the following conclusions are obtained. The model can recognize underwater organisms’ local, global, and visual features. Different recognition methods have certain advantages in accuracy, speed, and other aspects. The design here integrates deep learning technology and computer vision technology and applies it to the underwater field, realizing the association of the identified biological information with the geographic information and marine information. This is of great significance to realize the multi-information fusion of manned submersibles and the intelligent field of outdoor education. The contribution of this exploration is to provide a reasonable direction for the intelligent development of outdoor diving education.