Method for estimating click through rate based on convolution neural network

The invention discloses a method for estimating a click through rate based on a convolution neural network. The method comprises the following steps: establishing Hash tables for all elements of click instances to correspond with potential semantic vectors; for one specific click instance, indexing a corresponding potential semantic vector in the Hash table to obtain a click instance matrix which serves as an input matrix of the convolution neural network; conducting convolution and pooling of the convolution neural network to obtain a multi-layer convolution neural network; finally, multiplying a pooling layer by a full connection matrix, conducting a flexible maximum transfer function calculation to obtain an output layer; optimizing model parameters and inputting the model parameters, adopting a logistic cost function to measure performances of the model, and finally outputting an estimation probability of the click instance being put into each type. According to the invention, the method can mine global semantic interaction information which is important in a single advertisement and regional and global dynamic features in a sequence advertisement, which addresses the problem of current models that only regional or static features can be extracted.