Med den raske utviklingen av informasjonsteknologi blir informasjonsteknologiske applikasjoner stadig mer vanlige I denne artikkelen presenteres et system for analyse og prognose av aksjekurser basert

With the rapid development of information technology, information technology applications are becoming increasingly common. This article presents a stock price analysis and forecasting system based on Django and LSTM, which provides more accurate forecasts for investors in light of the current development in the financial industry. First, the context and current situation of stock price analysis and forecasting are presented, the limitations of traditional forecasting methods are discussed, and a forecasting model based on deep neural network LSTM is proposed. The principle and method used to create the model are described. Then, the design and implementation of the stock forecasting system based on the Django framework are presented, including functional modules and design interfaces. In traditional methods of stock forecasting, only forecast models based on statistical methods consider the impact of certain economic indicators, ignoring many other factors, leading to inaccurate forecasts. LSTM, as a neural network model with memory, can capture multiple patterns in time series data and improve the accuracy of stock forecasts. The proposed stock forecasting system based on the Django framework has good scalability and maintainability, and is divided into modules for system entry, user registration, data retrieval, preprocessing, model training, stock communication, etc., to achieve functions such as data retrieval, cleaning, and forecasting. By defining the hyperparameters in the LSTM model, such as the number of time steps, number of hidden layers, and number of training iterations, one can design and customize the learning model from different perspectives to determine the deep neural network structure. This article provides a reference for forecasting the direction of stock prices for ten national companies, with high forecasting ability and good stability for the developed system, and enables the delivery of more reliable stock forecasts for investors