Abstract
In order to obtain the scene information of the ordinary football game more comprehensively, an algorithm of collecting the scene information of the ordinary football game based on web documents is proposed. The commonly used T-graph web crawler model is used to collect the sample nodes of a specific topic in the football game scene information and then collect the edge document information of the football game scene information topic after the crawling stage of the web crawler. Using the feature item extraction algorithm of semantic analysis, according to the similarity of the feature items, the feature items of the football game scene information are extracted to form a web document. By constructing a complex network and introducing the local contribution and overlap coefficient of the community discovery feature selection algorithm, the features of the web document are selected to realize the collection of football game scene information. Experimental results show that the algorithm has high topic collection capabilities and low computational cost, the average accuracy of equilibrium is always around 98%, and it has strong quantification capabilities for web crawlers and communities.
Highlights
With the continuous development of football and modern science and technology, sports science and technology workers have carried out some statistics, analysis, and evaluation in sports. e primary task of scene information collection in general football matches is to collect information from various channels
A scene information collection algorithm of a general football match based on web documents is proposed to obtain the scene information in a general football match more comprehensively. rough the construction of a complex network and the introduction of local contribution and overlap coefficient of community discovery feature selection algorithm, web document features are selected to realize the collection of scene information in football matches. e superior results on a wide range of visual recognition problems suggest that our proposed model is a stronger backbone for visual recognition
In the above formula, the numerator represents the number of common nodes of communities Ci and Cj, the denominator represents the number of all nodes of Ci and Cj, and the set of adjacent points is marked as U. e implementation basis of the feature selection algorithm of scene information in football match based on community discovery is complex semantic network graph, and the threshold value of the algorithm is 0.7
Summary
With the continuous development of football and modern science and technology, sports science and technology workers have carried out some statistics, analysis, and evaluation in sports. e primary task of scene information collection in general football matches is to collect information from various channels. Intelligent computing research aims at bringing intelligence, reasoning, perception, information gathering, and analysis to computer systems [2,3,4] It provides a new way of thinking for the scene information collection on the football match. Taking football matches as an example, aiming at many real-time and high-precision tracking tasks of moving targets, the algorithm can only extract real-time game information. Still, it cannot collect relevant information from web pages, which is one-sided in practical application. Rough the construction of a complex network and the introduction of local contribution and overlap coefficient of community discovery feature selection algorithm, web document features are selected to realize the collection of scene information in football matches. An Information Collection Algorithm of General Football Matches Based on Web Documents
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