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With the maturity of the level of perceptual intelligence, artificial intelligence research has shown a trend towards cognitive intelligence [1]. As a representative field of artificial intelligence in perceptual intelligence, image object recognition has problems in the development of cognitive intelligence between humans and machines and between machines, such as difficulty in common knowledge and low scalability. To this end, a knowledge-based multi-target association discrimination framework is proposed. By introducing a knowledge graph, the target feature knowledge is semantically expressed and regularized associative storage, and a multi-target association detection and recognition method based on knowledge learning is constructed, which is dynamically on-demand. While calling the target detection model, iteratively update the knowledge graph associated with multiple targets, forming a two-way feedback learning loop. Finally, through related simulation experiments, the feasibility of the multi-target association discrimination method based on knowledge learning is verified, and a new idea is provided for improving the knowledge generalization and scalability of the image target recognition algorithm.