To address the value difference between different colors in the recycling process of waste plastic bottles, the study provides an efficient color recognition approach for waste plastic bottles based on a deep learning-upgraded YOLOv8n model to handle the problem of color recognition and sorting. Adding a Bidirectional Feature Pyramid Network (BiFPN) to the neck network for multi-scale feature fusion to improve the model's generalizability. The decoupled head structure of the head network uses only one Conv2d module for both branches, and a reparameterized convolution RepConv module is added at the front end of the branch to reduce computational complexity and improve training accuracy. Replace the CIOU loss function with the WIOU v3 loss function to improve the model's detection accuracy. Comparative trials demonstrate that the model suggested in the paper is superior to typical object detection models. The results showed that the model in the article had a 44.8% lower parameter count, a 34.6% lower computational complexity, and a 2.7% higher mean average precision at 50% IOU (mAP50) than the original model. When identifying the colors of waste plastic bottles, the frames per second (FPS) can reach 66, which is 40.4% faster than the original model. The colors of waste plastic bottles may now be detected in real time and with high accuracy.