火灾的发生给社会带来了巨大损失，森林火灾防治任务日益迫切，而全球变暖的趋势使得这一问题更加复杂。深度学习在各行各业发挥着重要作用，大量模型也被不断地设计和提出，且模型改进的方式多种多样。文中提出了EfficientNet-E模型，它利用更为先进的ECA模块（高效通道注意力模块）来替换EfficientNet模型中的SE模块，通过增强注意力机制性能提升模型的性能。相较于SE模块，ECA模块更好地保留了传输中的信息，使得数据特征在传输过程中保留更充分，从而能够优化模型。为验证EfficientNet-E模型的性能，以及EfficientNet设计思想在森林火灾识别问题上相较于传统模型的优势，文中选取了经典模型中的代表——ResNet和DenseNet作为对照参考，并结合EfficientNet和EfficientNet-E进行了相关实验。实验选用3 303张森林火灾、非火灾和烟雾图片。多轮试验结果显示，EfficientNet-E在识别森林火灾数据上的效果要好于常规的经典深度学习模型，且相较于原始EfficientNet的平均准确率（89.28%），EfficientNet-E的平均准确率（90.04%）有所提升，标准差更小，训练稳定性也更好，从而证实了改进后的EfficientNet-E性能更加优良。

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.