A framework named CBAM-EfficientNetB0 was proposed in this paper, aimed at addressing the issue of low accuracy in distracted driver behavior recognition under low-parameter conditions. CBAM-EfficientNetB0 integrates CBAM attention mechanisms, including Channel Attention Module and Spatial Attention Module. This enables the network to focus more on important feature information, suppress irrelevant or redundant features, thereby enhancing feature distinctiveness and expressiveness, while also reducing complexity and improving model training efficiency. By adopting the cross-entropy loss function to address multi-class classification problems, and after experimenting with the optimization of the model's optimizer to SGD, optimized learning rates, and momentum parameters were obtained, which not only improved recognition accuracy but also enhanced model convergence. CBAM-EfficientNetB0 achieves a 97.6% accuracy with a basic parameter set of 5.33 million on the State Farm Distracted Driver Detection dataset. The results demonstrate that compared to other four frameworks in the same category, it achieves better accuracy and performs well under low-parameter conditions. In the future, this technology will be able to assist drivers in safe driving.