A weather recognition method combining an improved ConvNeXt network and knowledge distillation is proposed to improve the accuracy of weather recognition in complex traffic scenes while achieving network lightweighting. Firstly， the ConvNeXt_F network was constructed， and the SimAm attention mechanism was added after each set of Block feature extraction of the ConvNeXt network to correct the weights of the extracted deep features and strengthen the ability to capture discriminative weather features. Secondly， during the network training， equalized focal loss （EFL） and mutual-channel loss （MCL） were aggregated as the total loss function by using the average occupancy ratio， eliminating the effect caused by data imbalance using EFL and reducing the difference of local detail features under similar weather using MCL. Finally， the knowledge distillation technique was used to migrate the weather classification knowledge from the ConvNeXt_F network to the lightweight MobileNetV3 network， which has a marginal loss of accuracy but significant reduction in the number of network parameters. The experimental results showed that compared with other algorithms， the proposed method achieved 96.22% and 84.8% accuracy on the weather-traffic dataset of Ningxia expressway and publicly-available natural weather dataset RSCM2017， respectively； the FPSs were 157.6 Hz and 137.6 Hz and FLOPs and Params were 0.06 G and 2.54 M. Compared with the original network， the recognition accuracy， speed， and lightness of the network were improved， making it better applicable to practical scenarios with limited storage and computational power.