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Unpacking inspection is an important part in the express scene. In order to prevent the presence of flammable and explosive dangerous goods in the package, the staff must carry out unpacking inspection of dangerous goods according to industry requirements before packaging. In this paper, target detection and key pose estimation algorithms are introduced into the human action recognition framework, and a deep learning-based human-object interaction behavior recognition algorithm is proposed. First, by improving the Gaussian mixture (GMM) background modeling method to detect moving objects, extract behavior keyframes, use OpenPose algorithm for pose estimation, and identify initial behavior categories; The algorithm detects the type and location of the object of interest, proposes a multi-person-multi-object optimal allocation algorithm based on the auction algorithm, and constructs the feature descriptor of the human-object interaction relationship; finally, the initial behavior label and the human-object interaction relationship are described. The sub-processes perform decision fusion to obtain the final recognition result. Taking the actual express scene as an example to verify and analyze the proposed method, the experimental results show that the method can accurately identify the unpacking inspection behavior in the complex environment with similar targets and multi-person interference.