复杂构件内部缺陷的类型识别对于保证装备制造质量及安全可靠运行具有重要的意义。针对现有深度学习模型用于缺陷类型识别时存在局部特征提取较差、缺乏考虑缺陷经验特性以及特征信息丢失的问题，提出了一种融合先验特征、全局特征以及ReliefF-Pooling策略的缺陷类型识别方法；实现了缺陷几何、纹理等先验特征与卷积神经网络全局特征的融合分析，并通过构建基于ReliefF-Pooling的特征优化方法，实现不同权重特征信息优化利用；最后，以某航天企业实际的复杂构件内部缺陷的射线检测为例进行了验证。试验结果表明，所提方法可以有效提升复杂构件内部缺陷的类型识别精度。

The internal defect type recognition of complex components is of great significance to ensure equipment manufacturing quality and safe and reliable operation. Aiming at the problems of poor local feature extraction, lack of considering the empirical characteristics of defects and loss of feature information in the existing deep learning model for defect type recognition, a defect type recognition method based on prior features, global features and ReliefF-Pooling strategy is proposed. The fusion analysis of prior features such as defect geometry, texture and global features of convolutional neural network (CNN) is realized, and the feature optimization method based on ReliefF-Pooling is constructed to optimize the utilization of feature information with different weights. Finally, as an example, the actual radiographic testing of internal defects in complex components in an aerospace enterprise is used to verify the proposed method. The experimental results show that the proposed method can effectively improve the type recognition accuracy of internal defects in complex components.