A nearness matrix problem is considered with two constraints—least square spectra constraint, symmetric and skew-Hamiltonian structure. It discusses two problems: (I) the set L of symmetric and skew-Hamiltonian real n × n matrices A to minimize the Frobenius norm of AX − X∧, where X, ∧ are eigenvector and eigenvalue matrices, respectively, and (II) find  ∈ L such that C −  = min A∈L ||C −A||, where || ·|| is the Frobenius norm. A general form of elements in L is given and an explicit expression of the minimizer  is derived. Perturbation theory of the nearest matrix is analyzed. A numerical example is reported.