The model presents a deep learning financial time series forecasting model, utilizing a long short-term memory network to model and predict stock market data. The LSTM architecture is initially presented, followed by its advantages in capturing mean squared error as an optimization loss function for long-term relationships in time series data. By utilizing grid search and cross-validation techniques, we identify the optimal hyperparameter mix and assess the model's reliability and strength. For the practical examination, we opted for the daily trading information of a specific stock spanning from January 1, 2000, to January 1, 2023, and employed the LSTM model for forecasting the stock value. The findings indicate that the LSTM model effectively monitors real price fluctuations and achieves accurate predictions. By examining the forecast error, we continue to confirm the model's efficiency and benefits when dealing with time series information. The research also explores how model performance and tuning hyperparameters affect prediction outcomes, and suggests that incorporating multiple data sources and refining the model structure can enhance predictive capabilities and generalization performance. In conclusion, this research outlines the potential uses of deep learning in predicting financial time series, suggesting it can be utilized beyond the stock market in areas like forex, futures, and cryptocurrencies. Through constructing and fine-tuning deep learning models, financial organizations and investors can acquire more precise and prompt market predictions, enabling them to develop more rational and efficient investment strategies, decreasing investment hazards, and enhancing investment gains.