In summary, we develop TENN, the novel Time-reversal equivariant graph neural network framework with consideration of time-reversal symmetry which helps to build spin potential and time-reversal aware spin electron Hamiltonian of magnetic materials. TENN-e3 is a time-reversal E(3) equivariant model and a general framework for magnetic materials with the spin effect while the popular E(3)-equivariant graph neural network (ENN) cannot deal with it. TENN-e3 can build time-reversal E(3) equivariant spin potentials and spin electron Hamiltonian that include both structural and magnetic moments degrees of freedom for both noncollinear and collinear magnetic moments cases. Also, the spin-orbit coupling can be considered or ignored in TENN-e3 in an adaptive situation. Meanwhile, with the time-reversal constraint on Hamiltonian, TENN-e3 can be used to generate the Hamiltonian of magnetic materials with strict time-reversal symmetry. As for the generalization of TENN-e3, it's expected to find wide use in magnetic materials including building spin potentials, spin electron Hamiltonians, and other related physical scalars and tensors with various symmetry. Spin-Allegro, as an instance of TENN-e3, is constructed to learn the spin interatomic potential. In the test, Spin-Allegro reaches high accuracy on the BiFeO3 spin-lattice dataset. The eminent performance, the capability of parallelism, and the high transferability of Spin-Allegro show its great potential in performing large-scale spin atomic dynamics.