Due to the non-linear and noisy nature of stock price sequences, stock price prediction has always been a challenging task. Many studies use decomposition algorithms to improve prediction accuracy, but these studies only focus on overcoming stock price nonlinearity and do not consider other price factors. To address the above issues, this paper proposes a stock price prediction method based on an improved empirical mode decomposition and A-LSTM hybrid neural network. This method introduces multiple data indicators and combines complementary set empirical mode decomposition algorithm with attention enhanced Long Short-Term Memory (LSTM). Firstly, this method utilizes the complementary set empirical mode decomposition method to decompose the original closing price of the stock, obtaining multiple Intrinsic Mode Functions(IMFs) and a trend term to reduce the nonlinearity of the stock price, while extracting multi-scale features of the IMF; secondly, the obtained IMF, trend term, as well as the highest, lowest, and closing prices are input into an attention enhanced LSTM to learn multiple stock influencing factors and mine their feature information; finally, the attention enhanced LSTM is utilized to learn long-term dependencies in features and dynamically adjust the weights of input features, highlighting key information, and outputting prediction results. The experimental results on two stock markets and four stock datasets show that the predictive performance of our research method is higher than that of the benchmark model, with good accuracy and stability, which can provide support for financial market analysis and investment decision-making.