Optimizing Neural Networks IoT Devices Based on Swarm Intelligence Algorithms to Predict Closing Prices of AI Companies’ Stocks

Prediction of stock prices has always been a hot research topic. Internet of Things (IoT) devices can collect a large amount of data in real time, including market trends, consumer behavior, economic indicators, etc., providing valuable references for stock price prediction. The stock market is the barometer of the national economy, and the prediction of stock price fluctuation can not only provide a reference for investors and enterprises to set reasonable goals, make decisions and risk management, but also have great reference significance for the government to supervise and manage the market and formulate policies. In this paper, we use the quantum swarm intelligence algorithm to optimize the neural network method to predict the stock closing prices of two listed artificial intelligence companies, Dawning Digital Creation and Yunchuang Data and then analyze the data through IoT devices. In the stock price prediction results, the quantum particle swarm has the highest prediction efficiency and accuracy value among the four quantum optimization models.