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Performance optimization algorithms based on node attributes are of great importance for sharding blockchain systems. Currently, existing studies on blockchain sharding algorithms consider only random selection sharding strategies. However, the random selection strategy does not perfectly utilize the performance of a node to break the bottleneck of blockchain performance.

This paper proposes a blockchain sharding algorithm called TOPSIS Optimization Sharding System (TOSS), which is based on entropy weight method, relative Euclidean distance and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). It defines a multi-attribute matrix to assess node performance and applies TOPSIS for scoring nodes. Then, an algorithm based on the TOPSIS method is proposed to calculate the performance score of each data node. In addition, an entropy weighting method is introduced to obtain the weights of each attribute to balance the impact of dimensional differences of attributes on the attribute weights. Nodes are ranked by composite scores to guide partitioning.

The effectiveness of the proposed algorithm in this paper is verified by comparing it with various comparative algorithms. The experimental results show that the TOSS algorithm outperforms the comparison algorithms in terms of performance improvement for the blockchain system, and the throughput metrics are improved by about 20% in comparison.

This study introduces a novel approach to blockchain sharding by incorporating the entropy weight method and relative Euclidean distance TOPSIS into the sharding process. This approach allows for a more effective utilization of node performance attributes, leading to significant improvements in system throughput and overall performance, addressing the limitations of the random selection strategy commonly used in existing algorithms.

The purpose of this study is to present the relationship between family support factors and Chinese college students’ online-startup thinking on live streaming platforms. Considering China's specific online entrepreneurial environment, this paper divides Chinese college students’ online-startup thinking according to the liberal–conservative thinking theory. This study classifies family support factors based on the tangible–intangible resource division theory. Different tangible and intangible factors have different impacts on their online-startup thinking.

This study tests 588 samples based on the partial least squares path modelling and variance-based structural equation modelling. This study promotes importance-performance map analysis to explore additional findings of influencing factors and provide suitable suggestions for Chinese college students and related departments.

Tangible family support factors, such as labour resources support, and intangible family support factors, such as verbal encouragement, can positively enhance Chinese college students’ liberal thinking to online-startup and decrease their conservative thinking. Meanwhile, according to importance-performance map analysis results, verbal encouragement from the intangible unit instead of financial resource support from the tangible unit has a higher total effect and performance on Chinese college students’ liberal thinking and conservative thinking.

This study draws on psychology research based on Chinese college students’ unique entrepreneurial mentality. This paper divides Chinese college students’ thinking in online-startups into liberal thinking and conservative thinking based on the liberal–conservative thinking theory. Meanwhile, according to the feature of Chinese family support factors, this paper classifies various elements based on the tangible–intangible resource division theory, which is helpful for scholars to understand that the student perceptions of the value of family support are critical to the success of the online-startup.