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The emerging construction of China’s industry park requires for a specific evaluation method for the local energy internet’s power utilization efficiency. This article focuses on the solar power exploitation from power supply reliability, energy utilization efficiency, solar power general efficiency, established a complete energy internet evaluation index system using improved analytic hierarchy process (AHP). Such method was proved to be more delicate to evaluate the solar-involved park energy internet comparing to traditional methods. The energy internet of a certain industry park was taken as an example for verification. It was found that the improved AHP method can objectively reflect the planning’s efficiency from multi-indicators. When evaluating from different perspective, focusing on a different characteristic of the regional power grid will lead to changes in the AHP evaluation weighting and the results. According to the evaluation result, the relevant suggestions to improve energy efficiency were also proposed.

Although commercial recommendation system has made certain achievement in travelling route development, the recommendation system is facing a series of challenges because of people’s increasing interest in travelling. It is obvious that the core content of the recommendation system is recommendation algorithm. The advantages of recommendation algorithm can bring great effect to the recommendation system. Based on this, this paper applies traditional collaborative filtering algorithm for analysis. Besides, illustrating the deficiencies of the algorithm, such as the rating unicity and rating matrix sparsity, this paper proposes an improved algorithm combing the multi-similarity algorithm based on user and the element similarity algorithm based on user, so as to compensate for the deficiencies that traditional algorithm has within a controllable range. Experimental results have shown that the improved algorithm has obvious advantages in comparison with the traditional one. The improved algorithm has obvious effect on remedying the rating matrix sparsity and rating unicity.