Fingerprint recognition is one of the important methods for identification or authentication, but there are some problems such as missing and false detection due to its small feature size and dense distribution. To solve the above problems, a fingerprint secondary feature detection method based on BRA(bi-level routing attention mechanism) was proposed. The BRA attention mechanism was embedded in Yolov8 to alleviate the problems of missing and false detection during fingerprint feature detection to enable a more flexible content awareness. The network structure of YOLOv8 was adjusted and a small-size target detection layer for fingerprint features was added. The experimental results show that the average accuracy of the YOLOv8-B network model Is increased, with mAP@0.5 increased by 4.3% and mAP@0.5:0.95 increase by 8.5%, reaching 98.2% and 74.9% respectively. And the detection speed remained basically unchanged, which can effectively detect the second-level characteristics of fingerprints and reduce the problems such as false and missing detection.