In many practical credit evaluation problems, a lot of manpower as well as financial and material resources are required to label samples. Therefore, in the process of labeling, only a small number of samples with category labels can be obtained to train classification models and a large number of customer samples is abandoned without category labels. To solve this problem, we introduce a semi-supervised support vector machine (SVM) technology and combines it with a multi-layer convolution kernel to construct a semi-supervised multi-layer convolution kernel SVM (SSMCK) for category customer credit assessment data sets. We first use a basic solution of the generalized differential operator to generate a base convolution kernel function in the H space, and then use the multi-layer strategy of deep learning to construct the multi-layer convolution kernel in the H and H space (called the family of multi-layer convolution kernel) by using the kernel functions in the H space. We further propose a semi-supervised multi-layer convolution kernel SVM algorithm based on the category center estimation and develop two novel SSMCK methods to improve the classification ability: the SSMCK based on multi-kernel learning (SSMCK-MKL) and the SSMCK based on alternative optimization (SSMCK-AO). Finally, experimental verification and analysis is carried out on three customer credit evaluation data sets. The results show that our methods outperforms or are comparable to some the state-of-the-art credit evaluation models.