To address the low efficiency in developing catalysts for CO2 hydrogenation to methanol, this study constructs and validates an intelligent performance prediction model based on large language model (LLM) and deep learning. First, a Large Language Model (LLM) to design structured prompts, achieving semi⁃automated and high⁃efficiency extraction of multi⁃dimensional catalyst data from literature. Subsequently, a Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN⁃GP) is employed to augment the sparse original dataset, effectively overcoming the bottleneck of data scarcity. Following data cleaning, feature engineering, and dimensionality reduction, a hyperparameter⁃optimized Multi⁃Layer Perceptron (MLP) is constructed as the prediction model. The results show that the optimized MLP model achieves high prediction accuracy on an independent test set, with R² values for CO2 conversion and methanol selectivity reaching as high as 0.972 3 and 0.969 3, respectively. SHAP⁃based feature analysis reveals that BET surface area and Cu⁃based catalysts are the dominant factors affecting catalytic performance, and also uncovered the unique dependency of In⁃based catalysts on metal content. This data⁃driven model, integrating LLM and WGAN⁃GP, provides a powerful tool for the rapid screening and rational design of novel catalysts, demonstrating the great potential of AI in catalysis research.