Patent value evaluation is a crucial tool for optimizing resource allocation and guiding intellectual property strategy decisions. However， traditional manual evaluation methods are limited by subjective expert experience and low evaluation efficiency， making it difficult for enterprises in the digital economy era to meet the large-scale demand for patent value evaluation. In recent years， machine learning technologies， with their powerful high-dimensional feature extraction capabilities， have provided a feasible technological approach to innovating patent value evaluation paradigms. However， existing studies mainly focus on small-scale models within single technical dimensions and do not fully explore the potential of Large Language Models （LLMs） for quantifying value indicators. Moreover， current methods struggle to handle cases where some patent data indicators are missing. To address the challenges of processing unstructured textual data and incomplete patent value indicators in the patent database of power grid enterprises， a deep learning-based patent value evaluation method for power grid enterprises was proposed. It used Large Language Model （LLM） technology to process unstructured textual information in power grid enterprise patents； adopted a Semi-Supervised Learning （SSL） paradigm to expand the labeled patent database used for training； and employed ensemble learning techniques to train the model on the power grid enterprise patent database and conduct patent value evaluation. Empirical results demonstrate that the proposed method can efficiently evaluate the patent value of power grid enterprises with low evaluation error.