With the continuous development of artificial intelligence technology, target recognition systems have been widely used in many fields, such as intelligent security, autonomous driving, and robot vision. However, existing target recognition methods still face the contradiction between accuracy and speed when dealing with complex scenes, different object categories, and small object detection. To solve this problem, this paper proposes a cross-platform target recognition system that integrates YOLOv7 and artificial intelligence methods. By combining YOLOv7 with traditional machine learning methods, the advantages of ensemble learning are used to improve the accuracy and robustness of target recognition. At the same time, the system design takes into account multi-platform compatibility and adopts cross-platform frameworks such as TensorFlow Lite and ONNX to ensure that the system can run efficiently on different hardware and operating systems. Experimental results show that the ensemble method significantly improves the recognition accuracy, especially in complex backgrounds and small object detection. At the same time, although the ensemble method brings an increase in computational burden, the system effectively balances accuracy and processing speed through resource scheduling and optimization. The system demonstrates its strong adaptability and application potential in a multi-platform environment.