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As a typical online word-of-mouth, online commentary has an important impact on consumer purchasing decisions. Therefore, it is very important to study the behavior of consumer online commentary. In this study, the user's behavior is quantitatively analyzed based on the real data of the website, and a user behavior dynamics model under the influence of marginal utility is constructed. The study found that the user comment behavior is subject to power law distribution, different levels of user activity are different and there is a tendency to rise first and then decline. This study provides a theoretical basis for the website to adjust customer relationship management in a timely manner.

With the rise of industrial interconnections, deep cross-border integration between different medical industries has begun. In the context of industrial convergence, the users’ medical business needs also show a trend of diversification and personalization. The phenomenon of multi-service resource crossing and multi-organization information barriers in the traditional medical supply chain lead to a lag in the medical resource recommendation time, which makes medical enterprises face the problem of reducing the efficiency of information resource flow in industrial interconnection businesses. This study thus constructed a collaborative recommendation model of medical services based on a cloud platform by mining the characteristics of dynamic medical service resources and user demand, and used a singular value decomposition algorithm based on time context to solve the model so as to achieve reasonable recommendation of dynamic multi-service resources in the medical supply chain. The results showed that the proposed collaborative recommendation model of dynamic medical business resources based on the cloud platform can effectively achieve medical business recommendations and provide ideas for reducing the operating costs of medical enterprise alliances under the condition of industrial interconnection and improving the efficiency of industrial resource interconnection.

coping with such disadvantages of particle swarm optimization algorithm and GA as being easy to run into local optima, the method of cooperative optimization is proposed to solve the job shop scheduling problem by combing the quantum-behaved particle swarm optimization and GA. The algorithm applied the parallel hybrid architecture of collaborative quantum particle swarm and GA, in which a kind migration operator was designed to associate all population, and the result shows that this algorithm has better answers and more rapid convergence.

The project management optimization for an important aspect of the scheduling scheme is reasonable to reduce costs, improve quality and shorten the cycle. Traditional project scheduling and optimization methods have been unable to fully meet the rapid development of modern project management needs. The PSO (Particle Swarm Optimization) is a simulation of birds the heuristic search algorithm mechanisms, which function optimization, constrained optimization, minimax problems, such as multi-objective optimization problem. It has become an important branch of the many related optimization fields. Although the project is to optimize the scheduling, many traditional methods can achieve good results, but the particle swarm algorithm can achieve a greater degree of optimization. In this paper, research on the particle swarm optimization of the basic principles of their algorithm for the initial exploration process, compared the effectiveness simulation of particle swarm optimization and traditional genetic algorithm in optimal scheduling of the project. Therefore, the original project plan with the optimal scheduling on the basis of introduction of particle swarm optimization algorithm can get better quality, shorter cycle and fewer costs, and ultimately get the entire optimal project cycle, project quality and project cost.