Current target intention recognition methods based on Bayesian network mainly focus on establishing a more reasonable network structure to improve the recognition accuracy, ignoring the influence of network parameter precision. Due to the complexity of the target's environment, the obtained target state data is uncertain, which causes the sample data used for Bayesian network parameter learning to contain uncertain information. However, in the current target intention recognition based on Bayesian network, the problem of uncertain information in the sample is not considered in the learning of network node parameters, which affects the precision of parameter learning and reduces the accuracy of target intention recognition. In order to solve this problem, a Bayesian network parameter learning method under uncertain samples is proposed, which can directly use uncertain samples to estimate parameters without losing sample data information, so as to improve the accuracy of parameter learning. Firstly, from the perspective of Bayesian network precise reasoning, combined with message propagation reasoning and discriminant learning methods, the conditional log-likelihood function under uncertain samples is established and used as the objective function of parameter learning. In order to alleviate the over-fitting problem under small sample sets, the L₂ norm normalization term of parameters is constructed according to the maximum entropy principle. Then the gradient descent method is employed to optimize the objective function to obtain the estimated parameters. Through the example test of target intention recognition based on naive Bayes classification, the results show that the proposed method effectively improves the precision of parameter learning and the accuracy of target intention recognition, and enhances the generalization performance of target intention recognition under small sample sets, thus the over-fitting problem is alleviated.