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Highlights • Aggressive pigs were separated from the herd and extracted continuously. • Head and 4 kink points of pigs were located and used as feature points. • Kinetic energy model was built by analysing motion of these 5 feature points. • Kinetic energy difference between adjacent frames was used to recognise aggression. • The proposed algorithm has higher accuracy, sensitivity, specificity and precision. Abstract The aim of this study is to build a kinetic energy model to recognise aggressive behaviours in pigs. Pig mixing experiments were performed 10 times. In each experiment, 7 pigs from 3 pens were mixed and then videos of the first 3 h after mixing and the 3 h after 24 h were selected as experimental data. A 30 h data of 5 groups of pigs were used as training set and a 30 h data of another 5 groups of pigs as test set. Firstly, according to connected area and adhesion index, aggressive pigs were separated from the herd and then key frame sequences with aggression were extracted. Secondly, head and 4 kink points of pigs were selected as feature points. By analysing motion of these points, kinetic energy of 2 aggressive pigs was calculated and kinetic energy difference between adjacent frames was selected as features. Finally, these features were trained by using hierarchical clustering to obtain the thresholds of high and medium aggression. These thresholds were used to design the rule of aggression recognition. By using the proposed algorithm, high aggression could be recognised with an accuracy of 95.8%, a sensitivity of 92.8%, specificity of 96.7% and precision of 90.2%, and medium aggression could be recognised with an accuracy of 92.3%, a sensitivity of 92.6%, specificity of 91.9% and precision of 93.0%. The results indicate that this algorithm can be used to recognise aggressive behaviours of pigs. [ABSTRACT FROM AUTHOR]