Stock market prediction and trading strategies have been extensively studied in finance and AI. Due to market volatility, it’s challenging for investors to achieve high returns. To address this, we explore deep reinforcement learning using LSTM-DQN and FC-DQN models for stock trading. LSTM-DQN combines Recurrent Neural Networks and Q-learning to capture stock data patterns, while FC-DQN uses a fully connected neural network. After applying discrete wavelet transform to the raw stock data for denoising and smoothing purposes, experiments were conducted. When comparing these two models in terms of accuracy and cumulative returns, the LSTM-DQN model outperforms the FC-DQN model. It’s evident that the LSTM-DQN model generates greater profits in terms of investment returns, making it a more suitable choice for investors. Finally, this paper conducts an analysis and discussion of the experimental results.