To address the multi-feature-driven nature of carbon price fluctuations, this study proposes an innovative carbon price prediction model that integrates multi-source features with deep learning techniques. The model combines the LSTM network with attention mechanism to effectively capture key temporal patterns, while quantifying news text through keyword frequency analysis to construct a news influence indicator. By incorporating an incremental learning strategy with experience replay, the model gains strong adaptability to dynamic market changes. Experimental results show that, compared with traditional models, the proposed model reduces the Mean Absolute Error (MAE) by 46.4% and improves the Root Mean Square Error (RMSE) by 17.1%. These findings validate the effectiveness of integrating news influence and multi-level mechanisms into carbon price prediction, significantly enhancing prediction accuracy.