This paper proposes a method for automatically identifying aggressive behavior in pigs using fixed-position monitoring, featuring a hybrid architecture that integrates object detection, tracking, and behavioral analysis. In the proposed architecture, a stationary camera captures images, with YOLO detecting pig locations and DeepSORT tracking their movements to identify individuals potentially exhibiting aggression. This process generates five-second video clips of individual pigs, which are then processed by a behavioral analysis module based on Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network. Experimental results demonstrate that the proposed method achieves approximately 90% accuracy in recognizing aggressive behavior in pigs on the test dataset.