The Borderline-SMOTE method is used to synthesize new samples with only a few samples on the boundary, which can improve the distribution of the samples. However, during the composition of the minority classes, the SMOTE did not consider the class information of the nearest neighbor sample, which often overlays the sample, resulting in poor classification performance. The Borderline-SMOTE method was proposed to improve this problem [33]. Support vector machine (SVM) was first proposed by [PERSON] et al. [34] as a solution to the dichotomy problem of linearly separable samples. In terms of recognizing abnormal gait, the SVM was successfully used in various pattern recognition problems [35]. Compared with other traditional learning models, the SVM has an excellent performance in solving few-shot learning problems [36]. Because it adopts the principle of structural risk minimization [37], the SVM model has strong generalization ability. This paper addresses the PD severity-level classification with a small sample set. The PD gait dataset from [PERSON] on PhysioNet [38] was used to demonstrate the proposed method. The dataset consisted of only 29 PD patients (15, 8, and 6 patients with HY ratings of 2, 2.5, and 3, respectively) and 18 healthy controls. The sample data was very small, so we considered three aspects to solve the small-sample learning problem: data, model, and algorithm [39]. When the training samples are insufficient, the neural network model with the objective of minimizing loss function tends to fit on a small number of samples, which results in low generalization capacity. However, many nonparametric methods do not need to train the optimization parameters, such as the embedded-learning (EL) method [40; 41]. EL is a nonparametric method based on a measurement in which the prior knowledge of training set is used as a design source. In EL, the samples are embedded into a low-dimensional space, which makes the samples of different categories in the low-dimensional space easier to distinguish. The embedded data then can be enhanced in the aspects of the discrimination degree and the balance among the sample size of classes to optimize the performance of the learner. The GRF data is divided according to the gait cycle, and then the categorized data is processed, and a series of gait characteristics is calculated. The variable importance is evaluated for the obtained characteristics by the RF method, and the variable of a bigger impact on the severity classification is reserved for further distinguishing features. In order to reconstruct the phase space by embedding the obtained gait characteristics with time delay, the data is mapped to the topological space. The topological characteristics of the obtained point-cloud data are analyzed by using the persistent homology methods to obtain the topological signature of the gait data, such as persistent bar code, persistent scatter plot and persistent state plot. However, these topology imprints are challenging to be used as input to machine learning. For this reason, the persistent scattergram topology marks obtained by important gait parameters is calculated as the persistent entropy [42], which is more suitable for machine learning. The SVM is employed for few-shot learning in gait analysis. In this study, a method based on permutation-variable importance and persistent entropy is proposed for the severity classification of PD. Based on the small dataset of gait, the dominant factors are extracted by permutation-variable importance, and the persistent entropy is proposed to transform the topological imprints into sample inputs more suitable for machine learning. The proposed method can fully improve the degree of differentiation between different disease categories and achieve a favorable effect, and has certain practical significance.