[Objective] Corn silage is a silage with high annual demand in dairy farms in Northeast China. Rapid and accurate evaluation of its nutritional indexes provides strong data support for the formulation of precise feeding formulas. [Method] In this study,120 corn silage samples were selected as the calibration set,and 30 as the validation set. Based on the previously established prediction models for dry matter （DM）and starch of fermented corn silage in different sample states （wet base, dried and crushed）,the database was further maintained. Meanwhile,near-infrared prediction models for crude protein （CP）,neutral detergent fiber （NDF）,and acid detergent fiber （ADF） of fermented corn silage in different sample states were established for the daily nutritional detection of fermented corn silage in dairy farms in Northeast China. [Result]（1）The established prediction models for CP,NDF,and ADF of fermented corn silage in different sample states could all predict the nutrition of fermented corn silage,and the actual prediction effect of CP was better than that of other nutritional indexes.（2）The prediction model for nutritional components of fermented corn silage after drying and crushing had better actual prediction effect than that of wet-based fermented corn silage.The NDF prediction model for wet-based fermented corn silage needed further data collection and optimization. [Conclusion] The near-infrared prediction models for CP,NDF,and ADF of fermented corn silage established in this study can be applied to the practical prediction of daily fermented corn silage in dairy farms in Northeast China,and the prediction model for dried and crushed samples is more effective.