Aiming at solving the difficulty of getting the best feature subset from high dimensional and the low identification accuracy of existing models, this paper proposes an algorithm for near infrared spectrum identification and measurement based on feature hierarchical selection and sample fusion degree. The paper firstly introduces the concept of jump degree, and proposes a feature hierarchical method to divide all the features into different subsets in terms of their importance to sample, which avoid the complicated process of deleting unrelated features one by one when constructing feature subset from the original feature data; At the same time, this paper improves sample fusion degree, while regarding it as the category judgment type of the improved KNN algorithm that take the place of probability, which has increased the precision of multiclass identification. The low identification accuracy was solved better though it. In order to verify the validity of our algorithm, five kinds of 382 representative tobacco samples were chosen as the experimental objects to build tobacco producing area identification models and 64 tobacco samples were chose as test samples; At last, with Root Mean Square Error of Prediction (RMSEP), Root Means Square Error of Cross Validation (RMSECV) and Correlation Coefficient (r) as the evaluation index of stability and identification accuracy as evaluation standard, the algorithm above made a comparison with other algorithms. The experimental results show that the model constructed by our algorithm has better stability with lower RMSEP (0.117), RMSECV (0.106) and higher r (0.973). The identification accuracy of our algorithm is the highest, reaching at 98.44%. The algorithm proposed in this paper has an excellent identification performance for high dimensional spectral data.