The gluing quality of thermal protection tile on hypersonic vehicles directly affects thermal insulation performance and flight safety. Current gluing process predominantly relies on manual operations strictly following established procedures. However

their dynamic complexity and strictly time-sequenced characteristics lead to frequent occurrences of operational sequence errors and component mis-assemblies

necessitating intelligent temporal behavior recognition and monitoring methods. To address these challenges

this study first defines the temporal behavioral characteristics of tile gluing process. Subsequently

we construct the SimA3D model for temporal behavior recognition by integrating the SimAM parameter-free attention mechanism into the C3D network architecture. A cosine annealing dynamic learning rate strategy is introduced in conjunction with an adaptive AdamW optimizer to enhance model convergence stability. Furthermore

a triple collaborative data augmentation strategy is proposed to expand sample diversity and input data complexity

effectively alleviating overfitting issues in small-sample temporal behavior recognition scenarios. Experimental results demonstrate that the SimA3D model achieves 98.32% recognition accuracy for gluing process behaviors

and the accuracy is improved by 19.9 percentage points over the baseline C3D network.