Research And Development Of Fresh E-commerce System Based On Django

With the rapid development of the Internet,traditional consumption patterns are gradually being broken down.Internet consumption is everywhere and green consumption is highly sought after.As an emerging area of e-commerce,fresh e-commerce has a low penetration rate in China and has a large space for development.Under the background of new consumption era,how to develop fresh e-commerce system quickly and efficiently is worth exploring and trying.The related technical theories and development process of fresh e-commerce system based on Django are introduced in detail in this paper.By studying related algorithms,the practical application value of the system is improved.The main research contents and innovations of the paper are as follows:(1)Taking the fresh e-commerce business as an example,the system front-end functions and background management functions are designed and implemented.The process of building the Django Web system is described in detail.Aiming at the characteristics of Django framework,how to perform secondary development based on the Django management template is researched.In the Web design process,a method of using a three-level inheritance structure is proposed for Django template design,and the code reuse rate is improved.In addition,the URLConf mechanism is researched and analyzed,and some practical URL design rules or methods are summarized and proposed,which improves the development efficiency of the system.(2)For the characteristics of the fresh e-commerce business,the enhanced entity relationship model is used to express the characteristics of data and the constraints between them in the database modeling process,and database information table of the system is designed.Business demand information can be intuitively reflected by expressing business operations abstractly as a data model.(3)The user behavior data collection system was built on the basis of the fresh e-commerce business system,and the related technical architecture and data acquisition scheme were studied in detail.Based on the business needs of fresh e-commerce,the data collection server and client were successfully constructed,and front-end visual analysis was performed on some of the collected data.The implementation of the user behavior data collection system provides strong data support for product recommendations.(4)For user personalized recommendation,the product recommendation system is constructed,and the relationship between the recommendation system and other systems is analyzed.Based on the application scenario of the system,the collaborative filtering algorithm based on goods is adopted.In view of the low coverage rate of the recommendation algorithm,a method of introducing item click feature influence factor to modify item similarity calculation is proposed.The actual test shows that the diversity of recommendation results is effectively improved through the application of improved algorithm and the high-quality commodity recommendation service is provided.