In recent years, abnormal operation behaviors in logistics have imposed significant losses and poor experiences on both enterprises and customers. Identifying diverse abnormal behaviors remains a significant challenge in this field. Therefore, it is crucial to propose an objective and quantitative monitoring and evaluation method. This paper utilizes a high-precision, compact, and low-power barometric pressure sensor to detect the internal pressure of small packages for rapid identification of logistics abnormal operation behaviors. The authors introduce a recognition fusion algorithm based on variance analysis and support vector machines (SVMs). This algorithm can identify various logistics abnormal operation behaviors, including unilateral extrusion, bilateral extrusion, treading, dropping, and stepping. The SVM model is employed to deeply learn and recognize these abnormal behaviors, achieving an average recognition accuracy of 98%. The proposed method outperforms five other methods, including Naive Bayes, by 4.9%, 2.12%, 2.76%, 4.46%, and 3.22% in detection accuracy. The shortest training time in the experiment is 2.6862 s, and the fastest classification per second can reach 3700 times. The barometric pressure sensor emerges as a promising approach for identifying logistics abnormal operation behaviors, contributing significantly to improving the current logistics security environment.