To address the challenge of recognizing unlabeled 3D models in open-domain， this paper proposes a multi-agent collaborative algorithm for open-domain multimodal 3D model recognition. The algorithm employs a reinforcement learning framework to simulate human cognitive processes. Within this framework， a multi-agent system is utilized to extract and fuse multimodal information， which enables a comprehensive understanding of the feature space while leveraging the similarity of multimodal samples to enhance model training. Additionally， a progressive pseudo-label generation method is introduced in the reinforcement learning environment. It dynamically adjusts clustering constraints to generate reliable pseudo-labels for a subset of unlabeled data during training， mimicking human exploratory learning of unknown data. These mechanisms collectively update the network parameters based on environmental feedback rewards， effectively controlling the extent of exploratory learning and ensuring accurate learning for unknown categories. Experimental results show that the average recognition accuracy of the method proposed in this paper on the three-dimensional dataset OS-MN40 reaches 65.6%. After transferring the method to the image domain， the classification accuracy on the CIFAR10 dataset reaches 95.6%， which provdies a universal and efficient solution for the research of open-domain three-dimensional model recognition.