## 1 Introduction  
The basic functions of neurons are to accept, integrate, transmit, and output information for information exchange. Neuronal clusters exchange information through various neurons to realize brain functions [1; 2]. Studying the classification of neurons is an important way to thoroughly understand brain information processing [3; 4]. The morphological and electrical characteristics of neurons are two important factors [5]. The electrical characteristic of neurons contains different action potential firing patterns. The potential spike pattern of the neuronal cells can be obtained in response to their puncture by a microelectrode; however, it is more complicated to distinguish the types of neurons by the spike firing characteristic of neuronal cells [6]. Dendritic and axonal trees stemming from the soma develop an extremely complex branching pattern that occupies and fills three-dimensional (3D) space [7]. The 3D morphological structure of neurons can be obtained by two-photon excitation fluorescence microscopy and fluorescence staining techniques. Since digitally reconstructed neuronal morphology is not only relatively simple to analyze, but also easier to obtain, researchers are more inclined to solve the neuron classification problem by analyzing the geometry data of neurons. At present, some researchers have applied machine learning approaches to solve the morphology classification of neurons, which have achieved many important results. [PERSON] et al. [8] analyzed the two-dimensional (2D) and 3D microscopic images of neurons, and obtained the geometric information of neurons to classify dopaminergic neurons in the rodent brain. They used different machine learning approaches for neuron classification such as support vector machine (SVM), backpropagation neural network, and multi-class logistic regression (LR). The neuron classification performances of different approaches have also been compared. [PERSON] and [PERSON] [9] proposed a neuron classification approach based on SVM where neurons were treated as irregular fragments, and the fractal dimension was calculated to describe the spatial structure of neurons. The fractal dimension was used as a new morphological feature of neurons and another 16 morphological features were added to the neuronal morphology classifier. The importance of fractal dimension feature to the neuron classification was validated by the experiments. [PERSON] [10] presented a neuron geometric classification approach based on a naive Bayesian (NB) classifier, which improves the accuracy of classification by the selection of classification attributes. Furthermore, [PERSON] et al. [11; 12] used label Bayesian networks to develop the classification method for GABAergic interneurons. [PERSON] et al. [13] proposed new features for neuron classification where three time series were derived from the 3D structure of the neuron. The selected neuron datasets were subjected to supervised classification and the results were compared by considering three sets of features: morphological, features obtained from the time series, and a combination of both. In addition, some neuron classification approaches did not apply to the category label information of neurons, so they used clustering techniques to realize the division of neuron morphologies. [PERSON] et al. [14] selected and analyzed 16 morphological features of neurons, extracted three main factors for cluster analysis, and then classified the neurons. [PERSON] and [PERSON] [15] used the expectation maximization algorithm to classify 1907 human pyramidal neurons into six categories, and then sorted the six types of data according to the weights to predict the growth process of human pyramidal neurons. From the above analysis, although researchers have proposed a number of neuron classification approaches, most of them are aimed at neuron datasets with a small number of morphological features, and the classification accuracy is not high. In this paper, we collected and selected the 3D neuron data of two different animals.