【摘要】 随着经济的飞速发展，许多人面临着购房困难和投资房地产高风险的问题。房屋是住房功能和投资效用的双重问题，因此房屋价格变得越来越复杂。房屋价格影响因素多，总体走势呈非线性。论文设计并实现了一个基于深度学习的房价预测系统，根据相关经济学原理选取房价影响因素，并对数据进行处理后使用该数据对神经网络模型进行训练、验证，以达到预测房价的目的。系统采用B/S结构，使用Django构建服务器，为用户提供房价预测服务、数据集查询、房价论坛等功能。更多还原

【Abstract】 With the rapid development of economy,many people are facing the difficulties of buying houses and the high risk of investing in real estate. Housing is a dual problem of housing function and investment utility,so housing prices become more and more complex. There are many factors affecting house prices,and the overall trend is nonlinear. A house price prediction system based on deep learning is designed and implemented,which selects the influencing factors of house price according to the relevant economic principles,processes the data,and uses the data to train and verify the neural network model,so as to achieve the purpose of predicting house price. The B/S structure is adopted in the system,and Django is used to build a server. The functions such as house price prediction,data set query,and house price forum are provided in the system.更多还原