Research On Multi-objective Analysis Of Human Nutrition Demand And Intelligent Recommendation Algorithm Of Agricultural Product

This year marks the first centennial year for China to build a moderately prosperous society in an all-round way.With the completion of a well-off society,people’s prosperity has been generally improved and their quality of life has been significantly improved.The people live a happy and happy life.However,malnutrition or over nutrition are common.How to get balanced nutrition from diet and keep healthy has become the common pursuit of the masses.Therefore,we use multi-objective particle swarm optimization and deep learning technology to study the nutrition analysis and personalized intelligent recommendation algorithm of agricultural products,which is of great significance and application value.First of all,the domestic and international research status of human nutrition demand recommendation method and its shortcomings are analyzed;according to the characteristics of multi-objective demand of agricultural products and human nutrition,the nutritional structure of various agricultural products and multi-objective demand characteristics of human nutrition are analyzed,and the energy,protein function and multi-objective characteristics of human nutrition demand are constructed by using multi-objective optimization theory The optimization model includes the balance objective function of all kinds of nutrients needed by human body.How to ensure that the overall nutritional intake of users from agricultural products is optimal on the basis of satisfying the user’s personalized choice,diversification of agricultural products and balance of various nutrients required by human body is the key problem to be solved in this paper.In order to solve this kind of problem,on the basis of the multi-objective nutrition demand optimization model,combined with the multi-objective particle swarm optimization algorithm,a multi-objective optimization algorithm for human nutrition demand based on particle swarm optimization is constructed.The inertia factor and neighborhood definition are set to improve the accuracy and efficiency of the algorithm.At the same time,the global search ability and local convergence speed are improved through better speed and location update formula.Secondly,in order to achieve the multi-objective personalized intelligent recommendation of human nutrition demand,convolution neural network is introduced.Through the input of a large number of orders and self-learning,the personalized preference of users’ nutrition demand and agricultural products matching can be better learned,and the personalized intelligent recommendation algorithm of agricultural products based on deep learning is established.Finally,the algorithm is applied to multiple original ecological agricultural product ordering platforms.After practical application,the algorithm can realize intelligent recommendation and effectively improve the accuracy of nutrition balance in product recommendation.