Drosophila is a crucial biological experimental teaching material extensively utilized in experimental teaching. In this experimental teaching, each student typically needs to manually identify hundreds of fruit flies and record multiple of each fly. This task involves substantial workload, and the classification standards can be inconsistent. To address this issue, we introduce a deep convolutional neural network that classifies the traits of every fruit fly, using a two-stage consisting of an object detector and a trait classifier. We propose a keypoint-assisted classification model with tailored training session for the trait classification task and significantly enhanced the model interpretability. Additionally, we’ve enhanced the RandAugment method to better fit the features of our task. The model is trained with progressive learning and adaptive regularization under limited computational resources. The final classification model, which utilizes MobileNetV3 as backbone, achieves an accuracy of 97.5%, 97.5% and 98% for the eyes, wings, gender tasks, respectively. After optimization, the model is highly lightweight, classifying 600 fruit fly traits from raw images in 10 seconds and having a size less than 5 MB. It can be easily deployed on any android device. The development of this system is conducive to promoting the experimental teaching, such as verifying genetic laws with Drosophila as the research object. It can also be used for scientific research involving a large number of Drosophila classifications, statistics and analyses.