Objective Under most circumstances wildfire warnings primarily rely on smoke or infrared sensors for detection. However these sensors are susceptible to environmental interference especially in large open spaces making it challenging to achieve precise fire alerts in open areas. Additionally superior flame detection models often have too many parameters and suffer from structural redundancy. Based on the above problems an improved VGG deep convolutional network architecture was proposed. Methods Pixel value adjustments were made based on mapping transformations. While ensuring classification accuracy L1 regularization was employed to ensure sparsity. Structural pruning was performed based on BN layers thereby reducing the storage data volume of the model and obtaining a streamlined model. Results Extensive simulation results demonstrate that this method maintains high detection and correction accuracy on wildfire architecture datasets under different pruning ratios. The improved model achieves an accuracy of 95. 29% at a pruning rate of 80% an increase of 0. 92% effectively addressing the issue of model over- parameterization. Through various fine-tuning training processes the accuracy of the improved model slightly surpasses that of the unpruned model while reducing the parameter volume by nearly twenty times. As the pruning rate increases the model?? s detection performance does not significantly decrease from the original precision level and in some cases it even slightly exceeds the original model?? s precision. This indicates that there is a significant amount of redundant weights during training. Conclusion This method substantially reduces the model ?? s storage volume while ensuring high classification accuracy demonstrating practical significance for application in embedded devices with limited neural network storage and computing capabilities.