## 1 Introduction  
Due to the traffic congestion caused by the rapid increase in the urban population and prosperous economic development, major cities worldwide are actively promoting subway construction [1]. Subway station projects are the most important component of a subway system, but the majority of safety accidents of subway system construction occur in subway station projects [2]. There are many potential safety hazards in subway construction, and safety accidents caused by these risk factors are likely to cause huge human and economic losses. In addition, early risk warning is an important component of risk management that links daily risk management and emergency risk management [3]. The accurate early warning of the construction safety risk level of subway station projects could effectively improve the emergency risk management of managers, thereby ultimately reducing the losses caused by construction safety accidents to the greatest extent. The construction content of subway station projects is complicated and includes excavation, support, the dewatering of the foundation pit, and the construction of the main and auxiliary structures of the station. In narrow and deep foundation pits, constructors of different professions operate various construction machinery. Therefore, the risk of subway station construction is characterized by many influencing factors. At present, the most commonly used method at the construction site is the checklist method [4]. Via the on-the-spot inspection of all hazard sources on the construction site, this method can directly reveal the construction safety risk status. However, this method has two serious shortcomings, namely (1) the cost is high, and the early warning efficiency is low, and (2) the result of risk early warning based on this method relies heavily on the subjective judgment of experts, so the accuracy is low. To cope with the deficiency of the checklist method in construction risk management, a large number of scholars have put forward a variety of early warning models based on the concept of multiple linear regression [5; 6; 7]. These methods make full use of the multivariate heterogeneous data of the construction site and realize the low-cost and rapid early warning of the construction risk. However, they assume that there is a linear relationship between the predicted data and the risk level and therefore cannot effectively cope with the complexity of subway construction safety risks [8]; thus, the risk early warning accuracy cannot meet the needs of engineering practice. With the vigorous development of machine learning and artificial intelligence in recent years, some scholars have applied machine learning technology to the early warning of construction safety risks. At present, the most representative models are the backpropagation neural network (BPNN) [9], the random forest (RF) [10], and the support vector machine (SVM) [11]. Related research results have shown that these new technologies could effectively improve the early warning accuracy of construction safety risks. However, the BPNN requires a huge training set to ensure the prediction results, and the data samples in engineering practice are usually small. The RF is not able to effectively cope with data with different attributes, and when the risk factors are divided into many values, the prediction results will be adversely affected. Although the SVM can sufficiently handle small sample data, it faces difficulty solving inequality constraints. When applied to the research of construction risk early warning, the SVM is characterized by some shortcomings, such as a too-large solution scale and insufficient computational performance. The least-squares support vector machine (LSSVM) is an improved SVM, and the core of the improvement is the solution of a set of linear equations obtained by the Kuhn-Tucker condition [12].