针对当前汽车零部件多特征提取智能溯源与真伪鉴别系统在提取多种汽车零部件材料表面纹理（如连续型、非连续型等）特征时存在的局限性，提出一种基于微观视觉和神经网络的汽车零部件防伪特征提取算法自动匹配技术。该算法结合人工智能自动匹配技术与微观视觉图像处理及神经网络混合算法，对汽车零部件进行防伪特征提取与识别。首先对采集的汽车零部件材料表面微观视觉特征图像进行频域变换、滤波和降噪处理；然后根据二维频域特征判断纹理类型（包括连续型、非连续型和轮廓型）；针对不同纹理类型，从算法库中选择适合的算法，提取并分析关键属性特征点。最后，构建深度学习框架，搭建汽车零部件微观视觉特征识别模型，并与先验特征库进行匹配，完成分类与真伪判定。实验结果表明，该算法能够有效提取并识别汽车零部件表面的防伪特征，准确率较传统方法有显著提升。通过与先验特征库的匹配，算法能够准确区分真伪，提供可靠的防伪验证结果。该方法能够有效解决多种汽车零部件材料表面纹理特征提取的复杂性，提升了防伪和溯源系统的准确性。基于微观视觉和神经网络的自动匹配技术显著增强了汽车零部件真伪鉴别的精确度，为汽车零部件的防伪提供了一种创新且高效的解决方案。

Aiming at the limitations of current intelligent traceability and authenticity identification systems in extracting multiple surface texture features (such as continuous, non-continuous, etc.) of automotive components, a micro-visual and neural network-based automotive parts anti-counterfeiting feature extraction and automatic matching algorithm was proposed. This algorithm integrated artificial intelligence-based automatic matching technology with micro-visual image processing and a neural network hybrid algorithm for anti-counterfeiting feature extraction and identification of automotive parts. Initially, the micro-visual feature images of the automotive component surfaces were processed with frequency-domain transformation, filtering, and noise reduction. Subsequently, the texture types (including continuous, non-continuous, and contour types) were determined based on the two-dimensional frequency-domain features. For each texture type, an appropriate algorithm was selected from the algorithm library to extract and analyze key attribute feature points. Finally, a deep learning framework was constructed, and a micro-visual feature recognition model for automotive parts was built, which was then matched with a priori feature libraries to complete classification and authenticity determination. Experimental results demonstrate that the proposed algorithm effectively extracts and identifies anti-counterfeiting features on the surface of automotive components, achieving a significant improvement in accuracy compared to traditional methods. Through matching with the a priori feature library, the algorithm accurately distinguishes between genuine and counterfeit components, providing reliable anti-counterfeiting verification results. This method effectively addresses the complexity of extracting various surface texture features of automotive parts, enhancing the accuracy of anti-counterfeiting and traceability systems. The micro-visual and neural network-based automatic matching technology significantly improves the precision of authenticity identification, offering an innovative and efficient solution for automotive parts anti-counterfeiting.