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For agricultural products and fast-moving consumer goods companies, the realities of too many retail terminals and limited human resources are the main problems faced by market visit tasks. Therefore, planning multiple paths for the sales man to obtain higher commercial value is a concern of the company. In this paper, for reducing the scale of data calculations, the large dataset is divided into several small datasets by meshing method, and then the path planning operation is conducted in the divided small datasets. Two path planning methods are proposed in this paper, Point’s distance-first path traversal method (PDF) and Point’s value-first path recursive method (PVF). Experiments show that algorithm PDF gives the distribution of points in the path is more intensive and the overall length of the path is shorter. And algorithm PVF gives the distribution of points in the path is more dispersed and the total value is higher.

In order to realize the automation of large data analysis and mining, a series of advanced algorithms, such as data cleaning and clustering, are used to deeply excavate the market sales data of agricultural and sideline products and it provide the necessary data basis for the decision layer of the enterprise. We designed and developed a visual analysis system of market sales data orients to products and business circles in the article. First, we clean and filter the original market sales data, and then establish the model based on clustering analysis in machine learning. Finally, the automatic display of analysis result views are achieved through excellent visualization framework technology. The experimental results show that in the actual sales data onto agricultural products, the characteristics of the business circle that the system automatically aggregates are obvious, and the combined display mode of the main and auxiliary views can show the characteristics of the sales data of a commercial circle from the macro and micro perspectives, respectively. It not only utilizes the ability of computer automatic analysis, but also fully exploits people’s cognitive ability of visual information and it helps people understand the information, knowledge and wisdom behind the big data of the market sales more intuitively and efficiently. Based on web visualization technology, a case of visual analysis of market sales data onto agricultural products and business circles is presented in this paper. And it has practical reference significance of other typical marketing analysis, especially for agricultural products market.

Modern agricultural products and FMCG industries are concerned about how to gain insight into market status information, product sales trends, and user characteristics from market sales data, so as to provide consumers with personalized product services. However, sales information often has the following characteristics: large amount of data, multiple data dimensions, varying values, and rapid changes in market information, and traditional analysis methods are difficult to analyze and use effectively. A multi-dimensional information visual analysis of shopping districts and products based on density clustering is proposed. Firstly, density-based clustering is applied to the geographic information on a large number of market retail households, and the most valued business district is obtained according to the multi-density ranking. Then use the user ID to associate order details to get a data-driven small targeted dataset. Finally, using a variety of visualization methods, multi-angle visualization analysis of multi-dimensional information is analyzed with business circles and products as the main body. Use real data onto case analysis. This analysis method can effectively reflect the laws of the sales market, development model, and consumption structure. It helps manufacturers and retailers to grasp the dynamic information and laws of market development in a timely manner and achieve the purpose of precision marketing and smart decision-making.

Despite the computers have developed rapidly in recent years, there are still many difficulties to obtain a large number of labelled data in many practical problems, for example, medical image diagnosis, internet fraud, and pedestrian detection. To deal with learning problems with only a few labeled data, a novel semi-supervised learning method combined with dynamic graph learning with self-paced learning mechanism is present in this work, namely SS-GSELM. Firstly, according to the loss value of labeled samples in each training, the algorithm selects the sample with the smaller loss value for learning, and then gradually adds the sample with the larger loss value during the training process until all labeled samples are trained. In particular, different weights are given to samples through a regularization function to adjust the importance of different samples on the model results. Secondly, the algorithm uses local consistency property as supplementary information to enhance the performance of the learning machine, so an adaptive graph matrix is constructed to retain data similarity information. To do this, an alternative strategy is proposed to update graph matrices and self-paced weights to adapt to the classifier. Experimental results on real data sets exhibit that the proposed method superior to the classic methods in classification tasks.