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This application provides a method, device, and electronic device for managing advertising campaign costs based on a dual-track online and offline classification system. By integrating a dynamic knowledge graph with a prompting learning model that efficiently fine-tunes parameters, it achieves high-precision automatic classification of advertising campaign cost categories. The method acquires the user-inputted cost description text and specified category, utilizes the dynamic knowledge graph for key entity extraction and multi-hop reasoning, and generates a first classification result and confidence score. Simultaneously, the text is input into the prompting learning model, which guides fill-in-the-blank prediction through soft prompts, outputting a second classification result and confidence score. Combining the confidence scores of both, a decision fusion rule is used to determine whether to trigger user correction prompts, and the knowledge graph and model parameters are dynamically updated based on user feedback. The system supports new entity recognition and incremental learning, forming a closed-loop optimization mechanism. This solution effectively solves the problems of inaccurate budget allocation, low execution efficiency, and difficulty in cost control caused by the fragmentation of data from online and offline channels, improving the accuracy, interpretability, and adaptability of cost classification, and achieving intelligent and refined advertising campaign cost management.