Driven by smart factories and Industry 4.0, the manufacturing sector is placing higher demands on the speed, accuracy, and traceability of anomaly diagnosis. However, existing approaches still rely heavily on manual interpretation and fragmented data, making it difficult to systematically integrate knowledge and automatically generate improvement plans. In this study, we propose an intelligent anomaly diagnosis system, AI-assisted Industry Anomaly Diagnosis and Correction (AIIA-DC) System, that integrates data digitization, statistical analysis, machine learning, and generative AI models to achieve end-to-end automation from data input to actionable recommendations. The system utilizes principal component analysis (PCA) with correlation coefficients (ΔCorr) and multivariate indices to explain the sources of anomalies, and employs a retrieval-augmented generation (RAG) framework to produce traceable and verifiable diagnostic reports. Using handwritten raw data from an electronic carrier tape manufacturing plant, the proposed method outperforms conventional PCA and standalone machine learning models in anomaly localization and interpretation of feature contributions, demonstrating higher accuracy and practical framework for a traditional industry pursuing for digitalization. The results provide process engineers with a tool for rapid diagnosis, reduced misjudgment, and improved decision-making efficiency, while also establishing a technical foundation for data-driven management and knowledge accumulation in smart factories.