Aiming at the data sparsity problem of collaborative filtering model, a variational autoencoder with clustering latent variable is proposed to process the implicit feedback data. The deep generative model can not only learn the feature distribution of latent variable, but also complete the clustering of features. The original data is reconstructed by multinomial likelihood, the parameters are estimated by Bayesian inference, and the regularization parameter is introduced into the model. By adjusting its size, it can avoid excessive regularization and make the model fit better. A nonlinear probability model has a better ability to model the prediction of missing scores. Experimental results on three data sets of MovieLens show that the proposed algorithm has better recommended performance than the other advanced baselines.