ABSTRACT Accurate stock market prediction is crucial for investors to formulate correct investment strategies. However, the non‐linearity, high dimensionality, and volatility of financial data pose significant challenges to existing stock market prediction models. To effectively address the complex datasets faced by stock market prediction, this paper proposes a new and more efficient deep learning hybrid model, KAN‐LSTM, based on the LSTM (long short‐term memory) and integrating the KAN (Kolmogorov–Arnold network). The hybrid architecture improves the learning process by replacing the original MLP (multi‐layer perceptron) with the KAN, overcoming the limitations of poor interpretability and fixed activation functions in LSTM. Prediction experiments conducted on multidimensional financial data in the stock market show that the KAN‐LSTM hybrid model outperforms the original LSTM in all evaluation metrics, demonstrating superior performance and more efficient prediction capabilities. Specifically, the MAE (mean absolute error) decreased by 2.43%, the RMSE (root mean squared error) decreased by 1.92%, the MAPE (mean absolute percentage error) decreased by 2.2%, and the increased by 19.08%.