<![CDATA[Augmented Reality (AR) technology is being widely adopted in various fields such as education, entertainment, and creativity. However, there are still some challenges to be overcome in recognizing and rendering three-dimensional (3D) objects accurately and in real-time. We implemented an AR system that utilizes deep learning techniques to recognize 3D objects with improved accuracy levels. Our approach involved training a Convolutional Neural Network (CNN) model using 3D object datasets captured from different viewpoints. The development included designing the network architecture, training the model, evaluating its accuracy, and integrating it into an AR platform based on Unity 3D and Vuforia SDK. The results indicated that the system could achieve recognition of the 3D objects with an average accuracy of 93.7%, precision of 92.4%, and recall of 91.8%, all while keeping response times below 0.8 seconds. Objects with complex geometries like cars and chairs had recognition rates above 94%, while those with similar textures had lower accuracy because of detailed surface complexities. It allows stable interactive visualization of objects in augmented reality even under different lighting conditions and camera angles. Combining deep learning with AR improves the quality of object recognition and provides a more realistic interactive experience. This paper discusses the advances made in AR technology toward better adaptability and efficiency, which can be applied to interactive education, industrial simulation, architecture, and medical fields.]]>
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