We propose a preconditioned squared Smith algorithm to solve large scale continuous-time Sylvester matrix equations. We firstly construct a preconditioner by using the alternating directional implicit (ADI) iteration, and transform the original equation to an equivalent non-symmetric Stein matrix equation. Then we apply the squared Smith algorithm to generate the low-rank approximation form with a Krylov subspace. Numerical experiments show that the algorithm has better iteration efficiency and convergence accuracy in comparison with the Jacobi iteration method.