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To solve the problem that the modeling process of model unknown Linear Time Invariant (LTI) system containing stochastic noise is complicated and the control law is difficult to be obtained, a data-driven predictive control method is proposed. Based on the theory of system behavior and balanced subspace identification, the non-parametric model of the controlled system is built only by using the measured system data, and the data-driven predictive controller is designed by combining it with the predictive control theory. For the bounded additive gaussian noise existing in the measured data, the influence of noise disturbance is reduced by introducing the slack variable and quadratic regularization of the data. The receding horizon optimization strategy is used to calculate the optimal control trajectory and apply it to the controlled system to realize the stability control of the system. The proposed control strategy is applied to a quadruple tank system. The simulation results show that compared with the data-driven subspace predictive control scheme, the pr\posed strategy has better dynamic performance. Meanwhile, the proposed strategy also has obvious advantages in anti-noise disturbance and stronger robustness.