To address the problems of data sparsity and cold start in traditional Collaborative Filtering （CF）， as well as errors caused by various transformations in the process of generating result matrices using matrix factorization methods， a Low-rank and Sparse Matrix Factorization （LSMF） paper recommendation method with mixed information enhancement was proposed. Firstly， pre-trained document-level representation learning and citation aware converter — SPECTER （Scientific Paper Embeddings using Citation-informed TransformERs） was used to learn the representation of papers， and then the similarity matrix among papers was calculated and constructed. Secondly， the similarity matrix and citation matrix were added together to form a mixed information matrix， and then the content similarity information and citation information were integrated into the paper-author matrix through matrix multiplication. Finally， the recommendation list was obtained by using LSMF model to decompose the paper-author matrix. Experimental results on ACL Anthology Network （AAN） and DBLP datasets show that the proposed method achieves better recommendation performance， and the way of introducing content information and citation information in the proposed method can be equally applicable to other matrix factorization models. For Non-negative Matrix Factorization （NMF）， Singular Value Decomposition （SVD）， Low-rank and Sparse Matrix Completion （LSMC）， and Go Decomposition （GoDec）， the Recall values of the top 30 recommended results （R@30） of these models with mixed information are increased by 18.72，7.43，11.53，14.62 and 20.58， 2.11， 7.91， 5.01 percentage points， respectively， compared with those of the original models on the two datasets.