Abstract: 【Objective】 Identification of main plants in desert grassland Seriphidium transiliense based on UAV remote sensing and CNN ResNet 18. 【Methods】 In this paper, the Seriphidium transiliense desert grassland, which was concentrated in Xinjiang and at the forefront of degradation, was taken as the research object. The low-altitude UAV remote sensing platform was equipped with a multispectral imager to collect grassland feature information. The ResNet 18 classical model in the deep learning model was selected, too. By setting two groups of training rounds of 40 rounds and 80 rounds, four groups of batch sizes of 8, 16, 32, 64 and five groups of learning rates of 0.01, 0.005, 0.001, 0.000,5, 0.000,1, the model classification performances under different parameter settings were compared and analyzed in order to explore the best parameters for the identification of main species in S. transiliense desert grassland community. 【Results】 The results showed that the overall classification accuracy of S. transiliense community was 83.65% and the classification accuracy of S. transiliense population was 84.21% and the population accuracy of arenarius was 81.15%, when the initial model hyper-parameter was set to 40 training rounds, 8 batch sizes and 0.001 learning rate. By adjusting the model hyper-parameters (the hyper-parameters were set to 40 rounds, the batch size was 32, and the learning rate was 0.0005), the overall classification accuracy of S. transiliense community was 83.73% and the population accuracy of arenarius was 83.78%, and the population accuracy of S. transiliense was 89.18%, which was 0.08% and 4.97% higher than the initial model, respectively. 【Conclusion】 The finding shows that the identification model with high precision, short time and stable performance can be obtained by adjusting the hyper-parameters.