## 1 Introduction  
High-entropy alloys (HEAs) [1; 2] are usually composed of four or more metallic elements. The high configurational entropy (\(\Delta\)Smix) of HEA makes it easier to form stable single-phase solid solutions (SS) than conventional alloys [3]. Theoretically, the special structure of HEA solid solution makes it possible for material researchers to design excellent mechanical, thermoelectric, and electrochemical properties. Also, different HEA phases, including the Face-Centered Cubic (FCC), Body-Centered Cubic (BCC), or Hexagonal Close Packed (HCP) phases, exhibit different properties [4]. In addition, the core effects of HEAs, such as the high entropy effect, sluggish diffusion effect, lattice distortion effect, and cocktail effect [5; 6], facilitate the transformation of HEAs into high-performance materials [7; 8; 9]. However, the multi-principal structure and complex formation mechanism of HEAs make it tremendously difficult to design high-performance HEA materials. There is an urgent need for a method to assist in the rapid exploration of the composition space of HEAs so that the properties of the HEA SS phases can be determined with the aid of different phases of HEA and the desired materials can be found. Traditional computational methods such as density functional theory [10; 11; 12; 13], ab initio [12], and calculation of phase diagrams [14] have accelerated the search for high-performance HEAs to some extent. However, these methods are computationally inefficient and require huge computational resources [15]. Some methods are not even accurate. Additionally, several parametric methods for differentiating the numerous alloy phases are mainly based on Hume-Rothery rules [16] and only judged by several thermodynamic parameters, which have been summarized by [PERSON] et al. [4]. For example, the possible phases of the alloys can be judged from the values of parameters such as mixing entropy (\(\Delta\)Smix), mixing enthalpy (\(\Delta\)Hmix), valence electron concentration (VEC), atomic size difference (\(\delta\)r), and Allen electronegativity (\(\chi\)). However, these parametric methods only use a single parameter, and it is difficult to obtain good alloy phase classification results. In recent years, a large number of studies have emerged that use data-driven machine learning (ML) [17; 18; 19] methods to predict alloy phases. These studies accomplish the classification of alloy phases using previously constructed empirical parameters as input features of ML and using the powerful nonlinear or linear mapping capability of ML to project the alloy phase classification problem into a high-dimensional space to make it linearly separable. [PERSON] et al. [20] used \(\Delta\)Smix, \(\Delta\)Hmix, VEC, \(\delta\)r, and \(\chi\) as input features for the K-Nearest Neighbors (KNN), support vector machine (SVM), and artificial neural network (ANN) algorithms to distinguish between SS phases and intermetallic compounds, respectively. [PERSON] et al. [21] used [PERSON]'s input features for six ML algorithms and analyzed the importance of the features using a self-organizing map, a scatter plot, a radar plot, etc. Finally, the alloy phases were predicted using ANN, and the accuracy was more than 80%. [PERSON] et al. [22] used the same features to construct an ANN model for differentiating SS, amorphous, and intermetallic compounds. The final accuracy under four-fold cross-validation reached 83%. All the above studies show that ANN has good predictions of alloy phases. [PERSON] et al. [23] used the elemental compositions and empirical parameters as input features to the adaptive neurofuzzy interface system, respectively, resulting in an accuracy of 84.21 and 80% in the classification of alloy phases, respectively. This suggests that it is possible to achieve good phase classification results by simply using combinations of elements to build ML models. [PERSON] et al. [24] utilized the SVM algorithm to fit five thermodynamic, configurational, and electronic parameters to these parameters.