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Modeling and understanding human motions are challenging in computer vision areas because the similar motions often occur at various time moments. The long-term dependences in observation data should be modeled to improve motion recognition performance. The conditional random field (CRF) is a powerful mechanism for large-span data modeling. In this paper, we present a new graphical model approach to effectively and efficiently implement CRF. Specifically, we integrate the dependent variables of a graph into a clique and build the junction tree for complex CRF structure with cycles. Using this approach, a tree inference algorithm is developed for finding the joint probability of all variables in the clique tree. In the implementation, we specify the continuous-valued hidden Markov model (HMM) parameters as the feature functions and evaluate the proposed junction tree CRF (JT-CRF) by using CMU Graphics Lab Motion Capture Database. The experimental results show that JT-CRF achieves the highest classification accuracies compared to the HMM, the maximum entropy Markov model and the linear-chain CRF.