Abstract: With the rise of machine learning,the number of operators increases rapidly,the solution space of composition operators to search increases,and the process composition time exponentially increases.How to reduce the search solution space,thus reducing the assembly time,and realizing the machine learning process composition to meet the functional needs of users has become the current research hotspot.This paper proposes a process composition method based on hierarchical tagging to support machine learning.Firstly,the label is extracted from the operator semantics,and the hierarchical label model is determined accor-ding to the semantic scope of the label.Secondly,according to the machine learning domain discovery label relationship,the domain composition model is established,and the final domain label model is determined according to the functional requirements determined by users.Finally,the domain operators are bound with tag semantics,the domain operator relationship model is determined,and the operators are composed according to the assembly rules to form all operator processes that meet the functional requirements of users.At the end of this paper,an example is given to show the feasibility of the method,and the result verification standard is proposed to show the correctness and integrity of the result.

Key words: Domain feature, Hierarchical label, Machine learning process, Semantic Web, Services composition