Since ancient times, what Chinese people have been pursuing is very simple, which is nothing more than "to live and work happily, to eat and dress comfortable". Today, more than 40 years after the reform and opening, people have basically solved the problem of food and clothing, and the urgent problem is housing. Nowadays, due to the storm of long-term rental apartment intermediary platforms such as eggshell, increasing the sense of insecurity of renters, as well as the urbanization in recent years and the scramble for people in major cities, this will make the future real estate market competition more intense. In order to better grasp the real estate price, let consumers buy a house reasonably, and provide a reference for the government to formulate policies, this paper summarizes the existing methods of house price prediction and proposes a house price prediction method based on mixed depth vision and text features. real estate related analyzes the development status the real estate at home M2, GRP, commonly existing resnet50 are used to preprocess the index attributes. The flow chart of the MVTs model is constructed, and the implementation steps of the proposed model are described in detail. Finally, we select 75% of housing samples as training, and the remaining 25% of the samples as test. We compare the prediction results of our model with other five models: autoregressive integrated moving average mode (ARIMA), grey prediction model (GM(1,1)), support vector regression (SVR), BP neural network and artificial neural network (ANN), the results show that the proposed novel MVTs model has higher prediction accuracy. Therefore, MVTs is more suitable for the house price prediction.