Design And Implementation Of Heterogeneous Resources Oriented Value Evaluation Module

With the vigorous development of the digital cultural industry,many cultural service resources have been transferred to online transactions.An important factor affecting transactions is resource pricing.The cultural service value evaluation system provides online value evaluation functions for various resources on the network,and helps to price resources scientifically.The study proposes a design and implementation scheme of a heterogeneous resources oriented value evaluation module,which integrates intelligent evaluation methods of various resources.When cultural service value evaluation system works,it calls the module to pass in features,and gets the evaluation result.The value evaluation module includes a data preprocessing sub-module,a model prediction sub-module,an evaluation result analysis sub-module and a model information management sub-module.The specific functions are as follows.The data preprocessing sub-module receives the original data from the system and performs corresponding preprocessing.For unstructured comment texts,the dictionary-based maximum matching method is used for word segmentation combined with domain characteristics,and HMM is added to identify unregistered words.Furthermore,the submodule uses CBOW for word embedding,which is convenient for subsequent model reception and processing.The model prediction sub-module is the core part of the value evaluation module.It builds prediction models according to the characteristics of different resources and predicts the value of resources.For cultural and tourism service resources,it builds an aspect-level sentiment analysis model based on LSTM,attracts the opinions from different attributes of resources in online user reviews,and calculates the value of resources;For intellectual property services,it builds a BP neural network based on adaptive particle swarm optimization,predicts the value using the service provider’s online comment data;For data set and data API,it builds a BP neural network based on adaptive genetic algorithm,and uses the resource’s features and trading platform performance data to jointly predict the value;For film and patent resources,it builds the random forest regression model based on grey relational analysis,first obtains high-quality data sets through grey relational analysis,and then trains the random forest regression model on the data set to improve the accuracy.The evaluation result analysis sub-module uses statistical methods to correct the obvious abnormal evaluation results;in addition,the G1 entropy method is used to calculate the weight of features in evaluation index system.And for cultural and tourism service resources,data generated from aspect-level sentiment analysis process is used to calculate intermediate results,which can describe resource value in multiple dimensions.The model information management sub-module uses Flask and Vue.js to build a visual interaction platform,which provides administrators with management functions for model information.Test each sub-module in the heterogeneous resource oriented value evaluation module.The functions run normally,and the output meets expectations.The value evaluation module can provide users with comprehensive evaluation results,and helps online platforms to reasonably price resources and promote transactions.