Aviation connectors play a vital role in complex electromechanical products. It is essential to have access to contact recognition within the connector model to facilitate the registration and guidance of intelligent aviation connector insertion operations. However, lightweight models often lack contact recognition, leading users to manually label the contacts, reducing efficiency and introducing errors easily. Therefore, this paper proposes lightweight model-based intelligent recognition of aviation connector’s contacts for AR guidance. The objective is to automate the contact recognition. Firstly, the hough circle transform is utilized for contact coarse positioning, followed by deep-learning using the positioned contacts as a dataset. Subsequently, the central contact’s centroid is determined, and contacts are categorized into concentric layers based on their distance from the central contact. Finally, the main dowel is positioned through corner detection, enabling sequential sorting of contacts within each layer based on the main dowel. Experimental validation demonstrates a 100% accuracy rate in contact recognition when utilizing this method, affirming the effectiveness of lightweight model-based intelligent recognition of aviation connector’s contacts.