Abstract
From the appearance of entrepreneurial narratives, it communicates the movement of innovatory suggestion and entrepreneurial madness on revenue deed in technology crowdfunding, outsearch the immediate percussion of innovatory hint on financier act, and the moderating sign of entrepreneurial emotion on the relationship between novelty and resources exploit. Through the mode of text-book analysis, the genuine technology design in JD crowdfunding was interested in bearing empiric trial of the mold. Neural networks have large-scale parallel, distributed storage and processing, self-organization, self-adaptation, and self-learning capabilities and are especially suitable for dealing with inaccurate and ambiguous information processing problems that need to consider many factors and conditions at the same time. The empiric arise shows that forward-looking neologism queue indubitably moves financier completion, and primitive neology suggestion negatively pretends resources accomplishment; pierce People's entrepreneurial emotion intensify the dogmatic outcome of incremental neology humor on financier work, but has no momentous regulatory performance on the relationship between fundamental novation cue up and revenue exploit. On this base, a nerve plexure pattern is further fabricated for equal analysis. The characteristic efficiency of retrogradation example and nerval meshwork fashion is amended. In addition, based on the sample data of the Dahuotou fair crowdfunding platform, the indicators of the extended resource evaluation are judged based on the video revenue duration of the design scheme, scheme dynamics, extended scoring, non-hidden technology, and employee inclusiveness. There is an interaction manifestation between video and operant allowance data; the penetration sign of the two logical recession molds is emended than that of the multilayer McCulloch-Pitts neuron mold and the divergent base duty design.
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CITATION STYLE
Xia, Z. (2022). Research on Performance of Public Welfare Crowdfunding Based on Neural Network Intelligent Model. Scientific Programming, 2022. https://doi.org/10.1155/2022/2532927
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