随着二次元文化与社交电商的深度融合,用户对个性化消费、互动体验和社群归属感的需求不断提升,传统电商模式在匹配精准度、社交互动和定制化服务方面面临诸多挑战。如何优化用户体验,提升平台运营效率,促进社群生态的稳定发展,已成为行业探索的核心议题。随着智能技术的发展,人工智能的深度应用为二次元社交电商提供了新的解决思路。本文分析传统二次元社交电商的痛点,阐述人工智能赋能二次元社交电商的现实意义,以“智创次元铺”为研究对象,探讨智能化手段对精准推荐、用户互动、内容创作及供应链管理等方面的优化作用,并提出未来发展建议,期望为二次元社交电商行业的创新发展提供有益参考。With the deep integration of two-dimensional culture and social e-commerce, users’ demand for personalized consumption, interactive experience and sense of community belonging continues to increase, and the traditional e-commerce model faces many challenges in matching accuracy, social interaction and customized services. How to optimize the user experience, improve the operational efficiency of the platform, and promote the stable development of the community ecology has become the core topic of industry exploration. With the development of intelligent technology, the in-depth application of artificial intelligence provides a new solution for two-dimensional social e-commerce. This paper analyzes the pain points of traditional two-dimensional social e-commerce, expounds the practical significance of artificial intelligence empowering two-dimensional social e-commerce, takes “Zhichuang Dimension Shop” as the research object, discusses the optimization role of intelligent means in accurate recommendation, user interaction, content creation and supply chain management, etc., and puts forward suggestions for future development, hoping to provide a useful reference for the innovation and development of two-di
针对中证1000成分股的投资策略构建,提出了基于支持向量机的多因子融合量价数据选股策略,应用于实时或模拟市场数据的回测中,以评估策略的有效性。本文对基于支持向量机的多因子选股模型进行改进,将量价数据融合到选股模型中,在多因子选股模型筛选出的股票基础上,进一步融合量价数据再次筛选,以期望获得更优的收益。回测结果表明,加入量价数据与未加入量价数据的模型对比,策略收益率提高了4.83%,策略年化收益率提高了11.43%,策略累积收益率与夏普比率显著优于多因子选股策略,最大回撤比之减小或者略高,预测涨跌更为接近实际股票涨跌趋势。实验结果表明:基于支持向量机的多因子融合量价数据选股策略应用在量化投资上,是十分有效的。In response to the construction of investment strategy for the constituent stocks of China Securities 1000, a stock selection strategy with the fusion of data of multi-factor, volume and price based on support vector machine is proposed which is applied to real-time or simulated market data backtesting, in order to evaluate the effectiveness of the strategy. We improve the multi-factor model of stock selection based on support vector machine, integrate the volume and price data into the model of stock selection, and further fuse the volume and price data to screen the stocks selected by the multi-factor model of stock selection to obtain more optimal returns. According to the backtest results, compared with the model with or without the addition of volume and price data and the model, the strategy return rate increases by 4.83%, the strategy annual return rate increases by 11.43%, the strategy cumulative return to sharp ratio is significantly better than that of the multi-factor stock selection strategy, and the maximum retracement ratio decreases or is slightly higher. The improved forecast moves more closely to the actual trend of stock movements. The results show th
