This invention discloses a multi-level and self-learning
analysis method and medium for testing data. The method includes: acquiring and
parsing product testing result data, and creating a general
intelligent agent for the parsed data; determining the type of product problem based on the general
intelligent agent, creating a corresponding specialized
intelligent agent according to the problem type, and assigning the product problem to at least one corresponding specialized intelligent agent; establishing at least one attribution model based on the specialized intelligent agents to perform in-depth
causal analysis of the
product testing data and outputting the product reasoning chain; and performing hierarchical self-learning analysis of the product problem based on the reasoning chain and an accumulated
knowledge base to obtain
key factors affecting the product and the correlation between these factors. This application improves the depth and accuracy of product problem analysis by assigning data to general and specialized intelligent agents, with the general intelligent agent identifying the problem type and the specialized intelligent agents conducting in-depth analysis and mining to output the product reasoning chain.