Increasing anthropogenic contamination poses a significant threat to groundwater security. Identifying potential contamination sources and apportioning their corresponding contributions are of vital importance for the prevention of contamination and management of groundwater resources. In this study, principal component analysis (PCA), modified grey relational analysis (MGRA), absolute principal component score-multiple linear regression (APCS-MLR), and positive matrix factorization (PMF) receptor modeling technologies were employed to evaluate the groundwater quality and apportion the potential contamination sources in the Lalin river basin, a main grain production district in the northeast of China. The contamination assessment with PCA and MGRA suggested that the groundwater in Lalin river basin was polluted due to human activities. The PCA method identified five and four potential contamination sources in wet and dry seasons, respectively, and the main sources were basically same. The APCS-MLR and PMF methods apportioned the source contributions to each groundwater quality variable. The final results showed that agricultural sources including waste water, agrochemicals and fertilizers were identified as the main sources of groundwater contamination both in wet and dry seasons. In addition, groundwater management strategies learned from the advanced experiences were discussed to protect the groundwater system in that region.