Mosquitoes are the transmission media of various diseases. The monitoring of vector mosquitoes is the key to preventing mosquito-borne diseases. Traditional manual identification methods of vector mosquitoes have high costs and low efficiency. Therefore, a classification method of vector mosquitoes under deep learning is proposed, which is based on transfer learning and three ImageNet pre-training models including fine-tuning ResNet18, DenseNet121, and MobileNetV2. K-fold cross-validation is adopted under small data sets with 900 mosquito images, and Aedes aegypti, Aedes albopictus, and Culex mosquitoes are classified to evaluate model performance. The average peak accuracy reaches 95%, 97%, and 97%, respectively. Finally, 344 mosquito images are predicted by using the model retrained under the data sets with 900 mosquito images. Specifically, the lightweight model MobileNetV2 achieves the highest precision, recall, and F1 score all of 0.95. According to the final prediction accuracy of the three models, it is concluded that the lightweight model MobileNetV2 performs better under a small number of data sets. The experiment changes the previous model fine-tuning modes. The learning rate of the model classification layer is set to be 10 times that of the previous layer, and the prediction accuracy of Aedes albopictus is improved by 5%–6% compared with previous experiments, which solves the training convergence problem of a small number of data samples and further expands the applicable environment for vector mosquito recognition.