The invention discloses a pipeline parallelization method for coarse-grained streaming applications. The pipeline parallelization method includes performing classic data profiling and dependency analysis on serial C-codes to acquire a task dependence graph, performing dependence transformation on the task dependence graph to acquire a directed acyclic graph, building a system feature graph, performing task scheduling on the directed acyclic graph according to the system feature graph and judging whether a task scheduling result meets performance requirements or not, if not, then aggregating and splitting task of the directed acyclic graph to acquire a new directed acyclic graph, selecting and calculating the highest-cost task of the new directed acyclic graph to acquire a new calculated hot spot region, returning to performing the dependency analysis again, segmenting and modifying the serial C-codes according to the task scheduling result so as to obtain parallelized C-codes, encoding to generate parallel executable files through an encoder, and loading the parallel executable files to a target hardware platform to execute. The pipeline parallelization method is adaptable to multilayer nested loop structures and capable of extracting parallelism of the multilayer loop.