A parallel algorithm for sparse general matrix-matrix multiplication (SpGEMM), swSpGEMM, targeting the new generation Sunway many-core architecture is proposed. The algorithm addresses the load balance issue caused by the distribution of nonzeros in input matrix, using a light weight parallel task partitioning. For the irregular memory access and inefficient instruction pipelining in accumulating the product, a hierarchical sparse accumulator has been proposed to maximize the utilization of local memory with different input matrix features and to relieve the instruction dependency in integer searching, resulting in more efficient use of the computing capability of the hardware. On large matrices from the SuiteSparse sparse matrix collection, the algorithm outperforms MKL on two Intel Xeon GOLD 6132 processors by 21.1% and cuSPARSE on NVIDIA A100 by 95.3%.