Surface self-intersection detection plays an important role in industrial software such as geometric modeling, CAD and CAE, which is essential for ensuring the geometric consistency and construction stability of the model. This paper presents a linear regression-based self-intersection detection method of Bézier surfaces. By constructing a sample set of Bézier surfaces, the input features and regression labels are settled based on spatial point pairs which associated with geometric and parametric distance. A linear regression algorithm of surface self-intersection is proposed to learn the mapping relationship between the point pairs. To address the imbalance in the distribution of self-intersecting and non-self-intersecting samples, the ADASYN adaptive sampling method is given for sample enhancement. The experimental results show that the new algorithm significantly reduces the misjudgment rate while maintaining high detection accuracy of surface self-intersection.