A method for predicting the trend of key user participation in Internet marketing activities

Through particle swarm optimization algorithm optimization algorithm, the method of improving time-domain convolutional networks of DenseNet networks is solved, and the problem of nonlinear non-stationary sequence of Internet marketing activities and intimate context is achieved, effectively predicting and analyzing the trend of key user participation.

CN119067712BActive Publication Date: 2025-03-28ZHEJIANG SCI-TECH UNIV
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Patent Information

Application Number
CN202411580482.2
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2024-11-07
Publication Date
2025-03-28
Estimated Expiration
2044-11-07

AI Technical Summary

Technical Problem

The existing Internet marketing activity trend prediction technology has the problems of nonlinear and non-stationary trend sequences and intimate context.

Method used

The particle swarm optimization algorithm is used to optimize the variational modal model, and the variable modal model is used to decompose the key user participation trend sequence into several subsequences, and the DenseNet network is used to improve the time domain convolution network and perform convolution operations to analyze the key user participation trend.

Benefits of technology

Adaptively processing nonlinear and non-stationary signals, splitting the trend sequence into multiple relatively stationary modes, solving the problem of non-linear non-stationary trend sequences, and improving the ability to reuse features through dense connections to avoid gradient vanishing and explosion.

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Abstract

The present invention relates to a method for predicting the trend of key user participation in an Internet marketing activity, which belongs to the field of Internet trend prediction, and comprises the following steps: S1. Collecting marketing data, and constructing a key user participation trend sequence according to the classification weights and interactive behavior scores of key users in the marketing data; S2. Constructing a variational modal model, using a particle swarm optimization algorithm to optimize the optimal parameter combination of the variational modal model, and decomposing the key user participation trend sequence into a number of key user participation trend subsequences through the variational modal model; S3. Using the dense connection method of the DenseNet network to improve the time domain convolutional network, each key user participation trend subsequence is respectively sent to the improved time domain convolutional network for convolution operation, and the key user participation trend is analyzed. The present invention solves the problem of nonlinear and non-stationary trend sequence of network marketing trend prediction, avoids the problem of gradient disappearance and explosion, and enhances the ability of feature reuse.
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