Abstract: 【Purpose/significance】Aiming at the instability and high error rate of library user clustering,a library user clustering meth?
od based on Markov model is proposed to improve the accuracy of library user clustering.【Method/process】The first-order Markov hy? brid model is used to construct the user action sequence model.The user behavior clustering is generated through the model to reflect the dynamics of user actions.The adaptive natural gradient algorithm is used to adaptively adjust its own step size according to the sep? aration state of user behavior,optimize the automatic model selection problem in model parameter learning,and realize the optimal li? brary user clustering.【Result/conclusion】The experimental results show that the proposed method can automatically select the model in the process of parameter learning when the actual number of clusters is less than the value.The proposed method has the largest number of clusters,can divide the largest value interval,and the lowest clustering error rate is 0.22%.The clustering performance is rela? tively stable,and the clustering results are more accurate,which meets the design expectation.【Innovation/limitation】The first-order Markov hybrid model is used to realize the clustering of library users.In the future,we will further study the high-order Markov compo? nent model that can consider the correlation between user sequences,so as to improve the accuracy and stability of clustering algorithm.