Title: GENERATIVE OF ORIGIN MODEL DISTRIBUTION MASKED WITH EMOTIONS AND TOPICS DISTRIBUTION IN HYBRID METHOD. Abstract: There is a vast amount of data in the form of natural signals in the world, and difficult expression processors are required to analyze such data. Traditional embedding methods are susceptible to generalization failure. In this study, we developed a classification model that creates and approximates an origin hypothesis model using limited emotions and topics. To solve the hypothesis, the proposed model utilizes dynamic learner modules. Using this mechanism, a text-based origin distribution representation learning model was designed. In order to simulate and generalize, we analyzed the experimental evaluation results via various natural language data sets and measured the corresponding performance. Thus, we demonstrated that the machine achieves the classification task more effectively by integrating learning distribution and multiple learning methods.