## 1 Introduction
The LSSVM reduces the solution difficulty and improves the solution speed, and it is a new application in complex problems such as power demand prediction [13], slope safety factor prediction [14], and soft soil settlement prediction [15]. In addition, in the application research of machine learning modeling, different algorithms reach different conclusions for different research problems. Therefore, it is very meaningful to introduce the LSSVM into the research of this paper. The kernel width and the regularization parameter of the LSSVM have obvious influences on its computational performance [12; 13; 14]. The genetic algorithm (GA) [16] or particle swarm optimization (PSO) [17] is often used to find the optimal parameter combination. However, the GA has some shortcomings, such as low computational efficiency and premature convergence, and PSO easily falls into the trap of the local optimum because of the lack of randomness of the change of the particle position. In addition, both the GA and PSO require the preset of too many calculation parameters, which is not conducive to finding the optimal parameters of the model to be optimized. Quantum-behaved particle swarm optimization (QPSO) replaces the displacement updating formula and velocity updating formula in the traditional PSO with the wave function of quantum mechanics [18]. Because the particles in QPSO appear in all dimensions of the solution space in the form of probability, it has a better global retrieval ability than the classical meta-heuristic optimization algorithms from the aspects of the prediction and optimization of turning surface roughness [19], image segmentation optimization [20], and economic scheduling optimization [21]. According to the \"no free lunch\" quantification in machine learning, although many scholars have already discussed the advantages of QPSO and the LSSVM in different fields, the research of their application in the research object of this paper is still valuable. Therefore, an early warning model of the construction safety risk of subway stations is constructed based on the LSSVM optimized by QPSO, and an empirical study on Chengdu Metro Line 11 is conducted. The contributions of this study include the following. (1) At present, most of the research results simply and roughly give the early warning index system of construction risk, lacking the relevant theoretical basis. Based on accident causation theory and the characteristics of subway station engineering, an index system is completely constructed to effectively deal with the risk complexity. To improve the engineering application value, the methods of obtaining each index are provided in detail. (2) At present, the prediction accuracy of related research cannot meet the engineering needs. Therefore, a novel early warning model integrating the computing advantage of QPSO and LSSVM is constructed to effectively deal with the strong nonlinearity of safety risks in subway station construction. (3) Different from the common simulation examples used previously, Chengdu Metro Line 11 is selected for empirical research. The findings provide new insights into the construction safety risk management of this project. The remainder is arranged as follows. Section 2 summarizes the related research. Section 3 describes the construction of the early warning index system for the construction safety risk of subway stations and describes the methods of obtaining the index data in detail. Section 4 proposes the early warning model in detail. Section 5 describes a case analysis. Section 6 discusses the computational performance of different optimization algorithms and prediction methods to highlight the advancement of the proposed model. Finally, the research results and limitations are summarized in Section 7.