Construction of a knowledge base system based on multimodal information fusion

This paper constructs an intelligent question-and-answer system for knowledge bases based on multimodal information fusion, aiming to achieve intelligent processing and retrieval of multi-source data such as images, text, and voice. The study analyzes the characteristics of multimodal data, proposes an end-to-end feature extraction and alignment scheme, designs a deep learning-based system architecture. The application of a cross-attention mechanism for feature fusion and a Transformer-based cross-modal reasoning model enables the system to realize core functions such as multimodal dialogue, knowledge question-answering, and similarity retrieval. Experimental results on publicly available datasets show that the system meets the expected goals in terms of accuracy, response time, and concurrent processing capacity, thereby advancing the development of intelligent question-and-answer technology.