Constructing meta-stable states and kinetic transition networks in different time scales can greatly improve the understanding of biomolecules. The thermodynamic, kinetic and dynamical properties in their high-dimensional conformational space and their intrinsic complexity can be honestly reproduced in the network representation without requiring a priori assumptions about reaction coordinates. Here we present a trajectory mapping method for top-down construct transition network among meta-stable states from long to short time scales. In the method, multiple (nanosecond or longer) simulation trajectories are generated in parallel, and each trajectory is mapped into a high-dimensional vector with the averages of lots of conformational functions along the trajectory as its components. The linear space spanned by the trajectory-mapped vectors has the same structure as that spanned by the conformational probability density functions of these trajectories: trajectories which located inside a single meta-stable state are mapped to the same vector, trajectories which transitioned among a few meta-stable states are mapped as linear combinations of the state-corresponded points with their occupation fractions in states as expanded coefficients. Therefore, clustering and simply linear algebraic analyzing on the trajectory-mapped vectors can be effectively identified meta-stable states, transition kinetics as well as transition pathways of the simulated systems. We demonstrate the method in polypeptides to get the folding dynamics and mechanisms.