## 1 Introduction  
With the rapid development of the economy, a large number of large-scale spatial structures such as stadiums and transportation hubs have emerged. At the same time, frequent engineering accidents have caused significant casualties and property damage, which increasingly attracts people's attention to the health status of engineering structures [1]. The identification of structural modal parameters is the basis of structural health monitoring; according to the way of obtaining structural modal parameters, the methods of modal analysis are as follows: Numerical Modal Analysis (FEA), Experimental Modal Analysis (EMA), and Operational Modal Analysis (OMA) [2]. For large-scale civil engineering, OMA is often used for modal parameter identification, because external excitation cannot be applied, or the excitation cost is too high [3; 4; 5]. The method requires no external excitation and is performed in the normal operating condition of the structure. Therefore, a variety of modal parameter identification methods based on environmental excitations have been developed [6; 7; 8], but in order to facilitate mathematical processing, these methods mostly assume that the input is stationary white noise, which is difficult to meet in practice [9]. Due to the influence of factors such as the unmeasurable excitation source, response noise pollution, and measurement error, the results of modal parameter identification have obvious uncertainties. Traditional stochastic subspace identification (SSI) is based on the time-domain state-space equation and is applicable to linear systems. In this method, the Hankel matrix is constructed based on the original data of the structural response, and then the Toeplitz matrix is obtained by covariance calculation, and the singular value decomposition (SVD) is performed on the matrix, and then the observable matrix and the controllable matrix are obtained, and finally, the system's modal parameters are identified [10; 11]. Under non-stationary excitation, the traditional stochastic subspace identification (SSI) method is prone to the problems of spurious modes and modal emissions [12]. Therefore, many scholars have made attempts to improve the stochastic subspace identification (SSI) method. [PERSON] et al. [13] combined the traditional random decrement technique with the stochastic subspace identification (SSI) method for modal identification of the Flying Cloud building. This method can effectively reduce the impact of noise, but the identification of frequency and damping ratio is unstable. [PERSON] et al. [14] proposed a covariance stochastic subspace identification method based on [PERSON]'s method. By using the Welch method and Hanning window, the signal is processed in the frequency domain to remove the effects of excitation and noise. Then, a Toeplitz matrix is generated, followed by eigenvalue analysis. Fuzzy C-means clustering analysis (FCM) and the modal mean phase deviation (MPD) index are used to identify the modal parameters of the structure. This effectively avoids mode omission and improves computational efficiency. [PERSON] et al. [15] proposed an optimized stochastic subspace identification (SSI) method based on singular entropy incremental fixed order and two-stage spurious mode rejection in steady-state diagrams. However, there is the problem of high accuracy in the identification of frequency and vibration mode, but low accuracy in the identification of the damping ratio. [PERSON] et al. [16] proposed an automatic identification method of stochastic subspace modal parameters based on spectral clustering and used it for the calculation of modal parameters of planar truss models and measured pedestrian bridges, which can effectively eliminate the spurious modes in the stability diagram with good robustness. [PERSON] et al. [17] proposed a fast truncated singular value decomposition method based on the Lanczos algorithm, which reduces the computation time of the stochastic subspace identification (SSI) method.