The occurrence of fires has brought huge losses to society. The task of forest fire prevention and control is becoming increasingly urgent, and global warming has made this problem more complicated. Deep learning plays an important role in all walks of life. A large number of models are constantly designed and proposed, and there are various ways to improve the models. Therefore, this article proposed the EfficientNet-E model, which uses the more advanced ECA module (Efficient Channel Attention module) to replace the SE module in the EfficientNet. It improves the performance of the model by enhancing the performance of the attention mechanism. Compared with the SE module, the ECA module better retains the information during transmission, allowing the data features to be more fully retained during the transmission process, thus enabling the model to be optimized. To verify the performance of the EfficientNet-E model and the advantages of EfficientNet’s design idea in forest fire identification compared with traditional models, this article selected representatives of classic models, ResNet and DenseNet, as comparison references, and conducted related experiments in combination with EfficientNet and EfficientNet-E.The experiment selected 3 303 forest fire, non-fire and smoke pictures.The results of multiple rounds of tests show that EfficientNet-E is better than the conventional classic deep learning model in identifying forest fire data, and compared with the original EfficientNet’s average accuracy of 89.28%, EfficientNet-E’s average accuracy (90.04%) is obviously improved. The standard deviation is smaller and the training stability is better, which confirms the improved EfficientNet-E’s better performance.