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The subalpine coniferous forest in west Sichuan is located in southwest China, which is affected by cloudy, rainy and foggy conditions, making it difficult to research vegetation classification by satellite images. (Objective) Therefore, this paper selects Wanglang Nature Reserve, a typical area of subalpine coniferous forest in western Sichuan, as the study area, and uses a multi-rotor UAV to acquire high-resolution RGB images of the northern part of the study area, combined with a convolutional neural network model for vegetation classification. (Method) To further explore the potential of convolutional neural networks on UAV remote sensing images, this paper selects the semantic segmentation method (U-Net) for classification, and also constructs vegetation classification models based on UAV images of different spatial resolutions and sample sets under different tile sizes, and establishes a forest fingerprint library. (Result) The results show that (1) the combination of UAV visible images and convolutional neural network model for classification can obtain high accuracy classification results, reaching the optimum at a spatial resolution of 5 cm and a size of 256×256, with an overall accuracy of 93.21% and a Kappa coefficient of 0.90. (2) The increase of ultra-high spatial resolution has limited improvement on the model accuracy. When the spatial resolution was increased from 10 cm to 5 cm, the overall accuracy of the model improved by 0.02 and the Kappa coefficient improved by 0.03, and the classification accuracy of the model did not improve significantly. (3) Choosing the appropriate size can improve the classification accuracy of the model. Under the spatial resolution of 5 cm, the overall accuracy of the model with the size of 128×128 was 82.30% and the Kappa coefficient was 0.76, and the overall accuracy of the model with the size of 256×256 was 93.21% and the Kappa coefficient was 0.90. (4) For the vegetation types that were underrepresented in the region, the influence of spatial resolution and tile size was much higher than that of the dominant tree species, especially the influence of spatial resolution was the highest. The producer and user accuracies for deciduous shrubs at a spatial resolution of 20 cm were below 70%. (Conclusion) This study shows that vegetation classification of subalpine coniferous forests in western Sichuan using UAV high-resolution RGB images combined with convolutional neural networks can achieve high-precision classification results, and explores the effects of UAV spatial resolution and tile sizes on the accuracy of convolutional neural network models, further tapping the potential of convolutional neural networks on UAV high-resolution RGB images to provide an automatic and accurate research method for vegetation classification in this region.