As aerospace engine manufacturing advances toward intelligent automation, traditional manual component identification methods suffer from low efficiency and high error rates. We present a vision-based automated recognition system that integrates image processing and deep learning algorithms for the identification of aerospace engine components. Nickel–cobalt alloy components (Inconel 718) are used as test samples. The system employs optical character recognition (OCR) and You Only Look Once version 8 (YOLOv8) algorithms for character detection and classification. Image preprocessing includes grayscale conversion, Gaussian blur, edge detection, and morphological operations to enhance image quality. To overcome the limitations of single-frame recognition under varying lighting conditions and complex backgrounds, a multiframe fusion mechanism is implemented to enhance stability and accuracy. Comparative experiments were conducted to evaluate algorithm performance in terms of recognition accuracy, processing speed, and computational efficiency. The system integrates a programmable automation controller (PAC) and positioning sensors for automated operation. Experimental results demonstrate an approximately 96% recognition accuracy with a processing time of 0.35 seconds per component, confirming the effectiveness of this approach for automated quality inspection in aerospace manufacturing environments.