As the user experience of a product is affected by both subjective and environmental factors of the users, the association model based on the results and influencing factors of the user experience evolves dynamic evolutions rather than simple static mappings. Therefore, this paper proposes the modeling method of the user experience based on high-precision adaptation, namely the adaptive variable coefficient PSO-RBF algorithm. The parameters are constantly updated through the introduction of the original parameters’ fitness values. This method overcomes the problems of low precision, weak generalization ability, and the tendency of falling into local optimum emerged in the traditional modeling methods. Related user experience data are collected by conducting user experience experiments. Via the verification of simulation experiments, the results demonstrate that this method is more precise and can make the generalization ability more effective in the condition of dynamic data modeling, proving the feasibility and effectiveness of this method. Thus, this approach provides a new research idea and method for user experience data mining.