Rice is one of the main grain crops in China and the pest stress on rice restricts the healthy and sustainable development of the rice industry. Therefore, it is of great significance to quickly and accurately identify the types of rice pest infestations. An improved YOLO version 8 nano (YOLOv8n) rice pest identification and detection method was proposed to address the issues of low accuracy, easy missed and false detections in rice pest detection. Firstly, the BiFPN pyramid structure is integrated for the integration of effective feature layers. And introduce an attention mechanism module based on squeeze and excitation (SE) between the backbone network and the neck network to improve the network′s feature fusion ability; introducing the Wise-IoUv2 loss function instead of the IoU loss function to reduce the regression accuracy issue of the original IoU loss function; further enhance the detection performance of the network. Extensive experiments were conducted on the rice pest dataset, and the results showed that the improved model proposed in this paper outperforms current mainstream algorithms in terms of mAP, Precision and Recall. Specifically, this method achieved 89.2% mAP, 92.5% Precision and 83.3% Recall. In contrast, the mAP of YOLOv5 is 84.1%, YOLOv8 is 85.2%, YOLOv9 is 86.4%, YOLOv10 is 85.1% and Faster R-CNN is only 59.75%. In terms of precision, our model outperforms YOLOv5 (88.4%), YOLOv8 (89.6%) and has a higher recall than all other models,demonstrating stronger object detection ability and stability. mAP has increased by 4% compared to the original YOLOv8n. Improving the YOLOv8n algorithm can meet the requirements of rice leaf pest identification and detection accuracy, while improving the detection ability of small and dense targets, thereby reducing missed and false detections. The improved YOLOv8n algorithm has certain advantages in accuracy compared to current mainstream algorithms and provides a better method for rice pest detection, which is of great significance for the prevention and control of rice pests.