The weight values in mixture distribution models usually depend on unknown or known parameters, which makes the model uncertain. To address this issue, we propose to determine the model weights based on Frobenius. Firstly, the multivariate Poisson distribution was truncated and homogenized to generate a multivariate Pseudo-Poisson distribution. Secondly, set function matrix of multivariate Pseudo-Poisson mixture distribution, multiple linear Pseudo-Boolean function matrix, multiple linear Pseudo-Boolean function matrix’s Frobenius norm were solved respectively according to the expression for countable mixture distribution. New weights were calculated and in turn a multivariate Pseudo-Poisson mixture distribution model was constructed. Finally, the correctness of the model was proved according to the normalization and nonnegativeness of the mixture distribution weights and the entire process of building model was demonstrated through simulation experiments. We also verified that arithmetic average is reasonable. The proposed model can provide a theoretical basis for applications and algorithm design of mixture distribution in machine learning.