Disclosed is a method and system for splicing and restoring shredded paper based on an extreme learning machine (“ELM”). The method includes: acquiring a shredded paper training sample to be spliced; extracting left and right boundary feature data of the sample; training an ELM neural network model according to the feature data to obtain a trained neural network model (“TNNM”); acquiring a shredded paper test sample to be spliced; extracting feature data of the test sample; selecting a first piece of to-be-spliced shredded paper; selecting, by the TNNM, a shredded piece with a highest degree of coincidence with the first piece; determining whether the shredded piece is correctly spliced to the first piece; if yes, splicing shredded paper until all shredded paper is spliced and restored; if not, adopting manual marking, and continuing to select, by the TNNM, shredded paper with a highest degree of coincidence with the first piece.