Learning engagement is an essential indication to define students' learning pacification in the class, and its automated identification technique is the foundation for exploring how to effectively explain the motive of learning impact modifications and making intelligent teaching choices. Current research have demonstrated that there is a direct link between learning engagement and emotional investment and behavioural investment, and it is appropriate and required to apply artificial intelligence to perform autonomous assessment. Unfortunately, the number of relevant research is limited, and the features of learning engagement in certain contexts have not been thoroughly examined. In this research, we highlight the features of a particular application scenario of learning engagement: the application scenario of learning engagement has to incorporate both the coarse-grained information of human body position and the fine-grained information of facial expressions. On the basis of this analysis, a fine-grained learning participation recognition model that suppresses background clutter information is presented. This model can effectively extract coarse and fine-grained information to improve the recognition of learning participation in real-world teaching situations. Particularly, the CNN-Transformer model suggested in this study employs CNN to extract fine-grained information of facial expressions and Transformer to recover coarse-grained information of human body position. Simultaneously, we gathered and categorised real teaching data based on the features of learning engagement situations and enhanced the data quality via crowdsourcing and expert verification. The experimental findings indicate that the CNN-Transformer model can accurately predict the learning engagement of unknown participants with a 92.9% rate of accuracy. Comparative trials reveal that the model's recognition impact is much greater than that of other sophisticated deep learning approaches. Our research offers a framework for future work on deep learning approaches in learning engagement settings.