Traditional fabric defect detection methods often rely on manual inspection or machine vision systems based on hand-crafted features. Deep learning models typically require large amounts of labeled data for training, and in practical applications, obtaining a large amount of high-quality labeled data is both challenging and costly. Therefore, this study combines deep learning and image processing techniques to propose a fabric defect detection method based on unlabeled compact deep learning. First, we train Convolutional Neural Network (CNN) compact deep learning using a pre-processed fabric defect sample dataset containing 18 categories to obtain an unlabeled deep learning model for fabric defect detection. Then a fabric defect detection method is developed based on this model, which is capable of performing detection of fabric image samples. Subsequently, the proposed method was validated using a real industrial fabric image dataset. The experimental results show that the method based on the unlabeled compact deep learning model improves the detection accuracy and efficiency by approximately 80% compared to traditional machine learning. Moreover, this method does not rely on a large amount of labeled data, offering better adaptability and broad application prospects.