Abstract
This paper mainly studies the application of artificial intelligence and machine learning in the field of stock investment. The principles and characteristics of KNN, k-Means, bisecting k-Means, and ANN algorithms are studied to compare the effects, similarities and differences of different algorithms. The algorithms are implemented through Python programs for stock analysis. According to the P/E ratio, dividend rate, fixed asset turnover rate, gross profit margin and other indicators of each stock, the stocks are classified and clustered to predict the stock development prospects and provide reference for selecting appropriate investment strategies.
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