The mainstream all-mappers of next-generation sequencing mostly use the seed-and-extend method. Due to high storage costs or long retrieval time of the long-seed index, most of these algorithms use short seeds, which results in redundant candidate positions and increases the time cost of alignment. We, therefore, propose an all-mapper based on long seeds, and a long-seed hash index with low storage costs and moderate retrieval time is designed. The long-seed hash index limits the hash space through modular operation and uses the Bloom filter to identify different seeds at the same storage location. Long seeds significantly reduce the number of candidate locations and thus lower the time cost in the verification phase. The experiments on human gene sequencing datasets reveal that the proposed all-mapper has higher time efficiency than the existing mainstream all-mappers while maintaining the same accuracy.