This paper addresses the challenges of intent recognition in collaborative Autonomous Underwater Vehicle (AUV) search missions, where multiple AUVs must coordinate effectively despite environmental uncertainties and communication limitations. We propose a consensus-based intent recognition (CBIR) method grounded in the Belief–Desire–Intention (BDI) framework. The CBIR approach incorporates fuzzy inference and deep learning techniques to predict AUV intentions with minimal data exchange, improving the robustness and efficiency of collaborative decision making. The system uses a behavior modeling phase to map state features to actions and a deep learning-based intent inference phase, leveraging a residual convolutional neural network (ResCNN) for accurate intent prediction. The experimental results demonstrate that the proposed ResCNN network improves intent recognition accuracy, enhances the efficiency of collaborative search missions, and increases the success rate.