To address the problems of high scene complexity, difficult recognition, and high false detection rate in detecting abnormal behavior of object passing through subway station barriers, a detection method is proposed that data fusion based on light detection and ranging（LiDAR）and camera sensors．A voxel difference algorithm is used to process LiDAR point cloud data, divide the detection area into voxel units, and establish a trigger mechanism for object passing．An object locking algorithm is employed to fuse data collected by LiDAR and cameras, supplementing depth information of human key points and locking onto target of object passing through barriers．The spatial-temporal graph convolutional network（STGCN）is lightweight-modified to reduce model complexity and computation time．A temporal trend attention（TTA）model is introduced to enhance the extraction of spatial-temporal feature changes in postures of object passing through barriers, forming the TTA-STGCN model to calculate the confidence of behavior occurrence of object passing through barriers．Collecting data of object passing through barriers through laboratory simulation and on-site in subway stations．Detection performance evaluation metrics are established．Training, validation, and testing of STGCN, STGCN-MIN, and TTA-STGCN models are conducted．In the training phase, the accuracy of the TTA-STGCN model improved by 3.73% compared to the first two, the overall loss decreased by 66.00%．In the validation phase, the accuracy of the TTA-STGCN model improved by 3.89% and 0.68% compared to the first two respectively, the overall loss decreasing by 58.95% and 58.48% respectively．In the testing phase, the accuracy of the TTA-STGCN model improved by 3.15% compared to the first two, the overall loss decreasing by 42.85% and 44.40% respectively．Field experiments show that the TTA-STGCN model' s accuracy improved by 2.99% and 3.49% compared to STGCN-MIN and STGCN models respectively, precision improved by 2.28% and 1.31% respectively, recall improved by 4.30% and 6.45% respectively, and F1 score improved by 0.033 5 and 0.040 4 respectively, demonstrating that the TTA-STGCN model significantly enhances the detection accuracy of behavior of object passing through barriers in specific subway station scenarios．