A method for constructing an alarm root-cause relational tree model of a cloud network, comprising: mining first alarm data of a traditional network based on a frequent itemset to obtain a first alarm tree model, and taking a traditional expert knowledge base as a label to extract a feature of the first alarm tree model to obtain a first feature parameter; self-defining a plurality of classification types based on a default classification rule, and determining a classification weight value corresponding to each of the plurality of classification types in the first alarm tree model by machine learning; and mining second alarm data of a network to be modeled in the cloud network based on the frequent itemset, and modeling the second alarm data using the classification weight value by machine learning to obtain a second alarm tree model, wherein the second alarm data are root-cause data; before mining first alarm data of a traditional network based on a frequent itemset to obtain a first alarm tree model, further comprising initializing, wherein initializing comprises: initializing a topological relation or a service relation or a professional relation or an alarm severity or an expert knowledge base of network element equipment according to a standard and a specification of an operator, and initializing a word segmentation list, wherein the word segmentation list contains keywords for segmenting a descriptive statement; after initializing and before mining first alarm data of a traditional network based on a frequent itemset to obtain a first alarm tree model, further comprising: extracting a rule base that generates primary and secondary alarm data from the expert knowledge base, wherein the rule base takes the primary and secondary alarm data as parent and child nodes of the tree model to form a parent-child relation tree model, the primary alarm data are the parent nodes and the secondary alarm data are the child nodes, and the rule base further comprises constraint conditions on the alarm data. mining first alarm data of a traditional network based on a frequent itemset to obtain a first alarm tree model, and taking a traditional expert knowledge base as a label to extract a feature of the first alarm tree model to obtain a first feature parameter; self-defining a plurality of classification types based on a default classification rule, and determining a classification weight value corresponding to each of the plurality of classification types in the first alarm tree model by machine learning; and mining second alarm data of a network to be modeled in the cloud network based on the frequent itemset, and modeling the second alarm data using the classification weight value by machine learning to obtain a second alarm tree model, wherein the second alarm data are root-cause data; before mining first alarm data of a traditional network based on a frequent itemset to obtain a first alarm tree model, further comprising initializing, wherein initializing comprises: initializing a topological relation or a service relation or a professional relation or an alarm severity or an expert knowledge base of network element equipment according to a standard and a specification of an operator, and initializing a word segmentation list, wherein the word segmentation list contains keywords for segmenting a descriptive statement; after initializing and before mining first alarm data of a traditional network based on a frequent itemset to obtain a first alarm tree model, further comprising: extracting a rule base that generates primary and secondary alarm data from the expert knowledge base, wherein the rule base takes the primary and secondary alarm data as parent and child nodes of the tree model to form a parent-child relation tree model, the primary alarm data are the parent nodes and the secondary alarm data are the child nodes, and the rule base further comprises constraint conditions on the alarm data.