Data mining technology is the product of the development of database technology, statistics technology and artificial intelligence technology. From a technical point of view, the main data mining methods include:

(1) decision tree method: decision sets are represented by tree structure, and these decision sets generate rules by classifying data sets. The most influential and earliest decision tree method in the world is ID3 method, and later other decision tree methods have been developed.

(2) Rule induction method: Through statistical induction, valuable if-then rules are extracted. Rule induction technology is widely used in data mining, among which the research of association rule mining is more active and in-depth.

(3) Neural network method: The biological neural network is simulated structurally, and based on the model and learning rules, three neural network models are established: feedforward network, feedback network and self-organizing network. This method can learn nonlinear prediction model through training, and can complete various data mining tasks such as classification, clustering and feature mining.

(4) Genetic algorithm: an algorithm for simulating biological evolution process, which consists of three basic operators: reproduction (selection), crossover (recombination) and mutation (mutation). In order to apply genetic algorithm, it is necessary to express the data mining task as a search problem, so as to give full play to the optimization search ability of genetic algorithm.

(5) Rough set method: Rough set theory is a new mathematical tool to deal with fuzzy and imprecise problems proposed by Polish mathematician Pawlak in the early 1980s. It is especially suitable for data simplification, data correlation discovery, data meaning discovery, data similarity or difference discovery, data pattern discovery and approximate data classification. In recent years, it has been successfully applied to the research fields of data mining and knowledge discovery.

(6)K2 nearest neighbor technology: This technology identifies new records through the combination of k recent historical records. This technology can be used for mining tasks such as clustering and deviation analysis.

(7) Visualization technology: information patterns, data associations or trends are displayed in an intuitive graphical way, and decision makers can interactively analyze data relationships through visualization technology. Visual data analysis technology broadens the traditional chart function and makes the user's analysis of data clearer.