Aiming at some problems of high subjective dependence and long time consuming in the process of high speed dry hobbing process parameters decision, a low carbon optimization decision method for high speed dry hobbing process parameters based on case-based reasoning (CBR) and optimization algorithms was proposed.At the same time,it was a method to achieve the low carbon of the hobbing processing.At the beginning,a back propagation (BP) neural net-works model was established based on the cases of high speed hobbing process,which could pre-dict the machining effect evaluation of hobbing processing.In addition,an improved K-means al-gorithm was used to obtain the similarity example extraction set for target process problem,and several process solutions were obtained to construct process parameter constraints.Moreover,the flower pollination algorithm (FPA) was applied to search the optimal process parameters for tar -get process problems,which took the minimum carbon consumption of the hobbing processing as the optimization objective.A high speed dry hobbing machine in an enterprise was used as an in -stance to verify the feasibility and effectiveness of proposed method.The experimental results in-dicate that the proposed optimization method is a very useful tool for achieving lower energy consumption and better processing effect.The method can also effectively avoid relying on process manuals,personal experience or cutting experiments so as to improve decision efficiency.Moreo-ver,the results also show that it is conducive to achieve high performance and low carbon operation of high speed dry cutting hobbing machine,which can provide important reference value for ma-chinery manufacturing enterprises to achieve low carbon manufacturing.