The invention discloses a method for forward of GPU acceleration of massive isomorphic sparse lower trigonometric equation sets. The method comprises the following steps of: (1) carrying out parallel layering on each line of a matrix L1 according to LU symbol decomposition results of a series of n-order linear equation set coefficient matrixes with a same sparse structure in a CPU, namely, a sparse structure of the lower trigonometric transform matrix L1, wherein L1 to LN have same sparse structure and parallel layering result; (2) transmitting data required by LU forward operation to a GPU by the CPU; (3) task distribution and equipment memory optimization: distributing forward operation tasks for the matrixes L1-LN into massive threads on the GPU to be executed, and optimizing memory using according to a merge access principle; and (4) starting a kernel function Batch-LUForward of layered LU forward operation in the GPU according to a layer increasing sequence. According to the method, the flow calculation speed can be improved and basis is provided for online analysis.