Intelligent Customer Service Coach Scoring System Design And Implementation

With the continuous development of Internet technology and deep learning technology,people are paying more and more attention to online education with low cost,high flexibility and high cost performance.The application of deep learning to online education systems has also been rapidly developed.The customer service business is an important part of communication between major companies and users,obtaining user experience,and serving users.Applying online education based on deep learning to the customer service business will greatly improve the efficiency of the customer service business and save a lot of costs.Therefore,creating an online customer service intelligent education system plays an important role.An intelligent scoring system combining online reasoning algorithm and offline algorithm training is designed.The online reasoning algorithm scores users' answers from five dimensions: correctness,completeness,affinity,enthusiasm,and keyword hits.Correctness judgment is realized by using pinyin similarity and semantic similarity;completeness analysis is achieved by word segmentation,stop word removal and edit distance calculation;affinity analysis is realized by affinity classification model,dispute word verification and score linearization;Positivity evaluation is realized through positivity classification model,negative word verification and score linearization;keyword hit analysis is realized using keyword hit rate calculation.Offline algorithm training provides a deep learning model for online reasoning,uses a CNN-based twin neural network and Manhattan distance to achieve a semantic similarity model,uses Text CNN-based text classification to achieve an affinity model,and uses Text CNN-based text classification to achieve a positivity model.Finally,the online reasoning algorithm and offline algorithm training are tested and evaluated respectively.Finally,the online reasoning algorithm can load model files and template files for offline training,and can give score results in different dimensions to meet functional requirements.The average F1 value of the five dimensions of the online reasoning algorithm is 88.75%,and the total time for a single sentence to process the five dimensions is 174 ms.The online reasoning algorithm can respond stably on a platform with a concurrent number of 10,000,and meet the accuracy and performance requirements.