[Aim]  To develop an efficient and convenient mini-program for the identification of Cerambycidae species at ports, thereby providing useful technical support for port quarantine work. [Methods]  This study designed and developed a mini-program for identifying members of the Cerambycidae at ports, with its core consisting of Cerambycidae detection models based on YOLOv5s and the improved ResNet50. The mini-program leverages the efficient and rapid advantages of the YOLOv5s model in object detection, allowing parameter optimization to be carried out based on image characteristics of the Cerambycidae to achieve precise identification of members of the Cerambycidae in images. The ResNet50 identification model was improved by integrating the CBAM (Convolutional Block Attention Module) to extract key features of the Cerambycidae and thereby overcome difficulties in identifying similar species. The integration of both models into the mini-program, together with a simple user interface, allows port quarantine staff to upload Cerambycidae images in real-time for detection and identification. [Results]  Verification with a large number of test samples in actual port scenarios indicates that the mini-program quickly, and accurately, detects and identifies members of the Cerambycidae in complex environments, and produces reliable results under different lighting conditions and in the presence of background interference, irrespective of the growth stage and posture of the insect. [Conclusion]  A mini-program developed for identifying members of the Cerambycidae at ports significantly improves the efficiency of port quarantine work, thereby helping prevent the establishment of alien Cerambycidae. This new program promotes the development of intelligent identification technology for harmful organisms at ports, and has good practical application value and potential for promotion.