Aiming at the problems of large computation time and low classification time, this study presents an improved parameter estimation model for LDA by using the method of tensor decomposition, which can collect, classify, and mine massive network data. Using the method of moments, the LDA model calculation is transformed into low-dimensional tensor decomposition, and the parameters are transferred by decomposition and reflection. The large data platform Spark is used for distributed computation. The experimental results show that the model has been improved in terms of running time and perplexity, and the classification information display is more intuitive, which is more suitable for large-scale network data classification.