Situated in southern China, Zhaoqing City is a part of Guangdong Province, China. The total administrative area of the city covers 14,891 square kilometers. The data of China’s seventh population census in 2020 showed that the permanent resident population in Zhaoqing City reached up to 4,413,594. Meanwhile, Zhaoqing is one of the cities in the Guangdong-Hong Kong-Macao Greater Bay Area. House price analysis and prediction carried out against Zhaoqing City will have directive significance for relevant policies formulated by the local government, residential investment or purchase of consumers, and prediction of house price trend as well as business decisions made by enterprises. By virtue of machine learning and statistical theory, the house price in Zhaoqing City from 2010 to 2020 will be researched, and the house price prediction model of Zhaoqing City will be constructed in this paper with several variables including GDP, proportion of tertiary industry, income of urban residents, fiscal revenue, land price, investment volume in real estate development, permanent resident population, population density, and proportion of urban population in net migration. First of all, the methods of correlation analysis will be utilized, to select variables that are highly correlated with house price data based on correlation coefficients. Then, the model will be constructed for predicting the house price on the basis of multiple linear regression analysis that is conducted with selected variables. Finally, the prediction model will be adjusted gradually based on data with different correlations selected from available data, to realize better imitative effect and more precise predictive effect and select optimum prediction model. By means of the above model, the house prices of Zhaoqing City in 2021 and beyond will be predicted accurately, with preferable fitting effect and prediction effect.

Today, with the rapid growth of Internet technology, the changing trend of real estate finance has brought great an impact on the progress of the social economy. In order to explore the visual identification (VI) effect of Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) algorithm based on neural network optimization on China’s real estate index and stock trend, in this study, artificial neural network (ANN) algorithm is introduced to predict its trend. Firstly, LSTM algorithm can effectively solve the problem of vanishing gradient, which is suitable for dealing with the problems related to time series. Secondly, CNN, with its unique fine-grained convolution operation, has significant advantages in classification problems. Finally, combining the LSTM algorithm with the CNN algorithm, and using the Bayesian Network (BN) layer as the transition layer for further optimization, the CNN-LSTM algorithm based on neural network optimization has been constructed for the VI and prediction model of real estate index and stock trend. Through the performance verification of the model, the results reveal that the CNN-LSTM optimization algorithm has a more accurate prediction effect, the prediction accuracy is 90.55%, and the prediction time is only 52.05s. At the same time, the significance advantage of CNN-LSTM algorithm is verified by statistical method, which can provide experimental reference for intelligent VI and prediction of trend of China real estate index and property company stocks.