Abstract
The development of science and technology has laid a solid foundation for the economic informatization of agriculture, and at the same time it brought technical guarantee for the development of agriculture, and the development of agriculture has provided an important material foundation for the development of science and technology. How to identify and deeply study agricultural economic informatization, give early warning to risk information, and ensure the steady development of the whole industry has become a key issue in the application of Internet technology in the field of agricultural development. This paper studies the present situation of agricultural economy informatization development process and applies support vector machine to forecast regional economic development level. The warning limit of agricultural economic growth rate is obtained on the basis of warning situation and warning indicator in early warning index system. The economic early warning model is established based on the support vector sequential regression method, and then the data is trained by MATLAB software to verify the rationality of the early warning model, and the accuracy and corresponding error of the model are given. Experimental results show that the prediction accuracy is 99.3%, the error is less than 0.05, and the prediction effect is relatively ideal, for agricultural economic intelligence information to provide accurate warning and agricultural economic research agricultural commercial development to provide support.
Highlights
In the development of information age, information management mode can promote the orderly development of all work
In order to improve the level of agricultural economic management information, the establishment of a perfect information platform can improve the sharing degree of information resources and improve the utilization rate of agricultural information. e rapid development of agricultural information technology can only be promoted after ensuring that agricultural information management is really implemented in every link
In order to enhance the applicability of the model and improve the accuracy, the following processing is done in data entry in this paper: the original sample is divided into training set and test set according to the ratio of 7 : 3 which is most commonly used for small samples, and 70% samples are randomly extracted to form the training set, and the remaining 30% are automatically grouped into the test set of the same group. e specific random process is to use the sample random function in the random module of Python language program to generate random numbers in each category and select samples with corresponding serial numbers to form test sets
Summary
In the development of information age, information management mode can promote the orderly development of all work. E development of rural economy promotes farmers to change their ideas and promote informatization to permeate agricultural production and, at the same time, establish and perfect the scientific and reasonable agricultural development operation mechanism, so as to effectively enhance the ability and level of agricultural economic development. It is necessary to establish a timely service system and agricultural monitoring and early warning system according to the actual situation of rural areas in each region and market analysis. Traditional early warning methods are often limited by expert experience and simple mathematical models, which make it difficult to achieve good results in dealing with high-dimensional features, small samples, and highly nonlinear models. In the small sample, high-dimensional, nonlinear data space, support vector machine can make full use of the information provided by various features; it has good generalization ability in the learning process. Support vector machine training is suitable for short-term prediction with less data and less sample demand and modelling workload
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