Evaluation Model of Click Rate of Electronic Commerce Advertising Based on Fuzzy Genetic Algorithm

In order to improve the ability of quantitative evaluation of e-commerce advertising click rate, a model of e-commerce advertising click rate evaluation based on fuzzy genetic algorithm is proposed. The big data information sampling model of e-commerce advertisement click rate evaluation, based on the mining result of e-commerce advertisement click rate evaluation information, carries on the adaptive mining and fusion clustering processing to the e-commerce advertisement click rate evaluation data, extracts the similarity information of the e-commerce advertisement click rate distribution set, carries on the e-commerce advertisement click rate adaptive evaluation according to the similarity contrast method, carries on the feature coding in the e-commerce advertisement click rate evaluation process through the fuzzy genetic optimization method, establishes the e-commerce advertisement click rate reliability feature distribution function, combines the statistical feature analysis and the fuzzy feature cluster analysis method to evaluate and predict the e-commerce advertisement click rate. The simulation results show that this method has better adaptability, higher accuracy and better convergence.