本文聚焦新闻消费情境中“算法厌恶”，采用对照实验，系统考察新闻消费者对算法推荐与人工推荐文本的主观评价差异，并进一步探讨人口学特征与内容偏好对推荐偏好的调节作用。实验共有800名参与者，通过李克特量表度量推荐意愿、个性偏好和整体评价三个维度，采用独立样本t检验、多元线性回归及路径分析进行统计处理。结果显示，整体上人工推荐显著优于算法推荐，在用户整体评价维度上表现出更高的主观满意度，验证了“算法厌恶”在我国新闻消费领域中的存在。尽管在推荐意愿和个性偏好维度上，算法组得分普遍偏低，但差异未达到统计显著水平。多元线性回归及交互效应分析显示，职业、性别、每日阅读新闻时间及是否主动搜索新闻等变量对不同推荐方式下的主观评价均有显著影响，特别是在“职业×推荐方式”和“内容偏好×推荐方式”方面，部分维度呈现显著或接近显著的交互效应，表明职业和新闻内容偏好能够调节用户对不同推荐方式的主观评价。例如，对国际新闻偏好高的用户，算法与人工推荐组间的满意度差距有所减小。三维度路径分析显示，整体评价在个性偏好对于推荐意愿的影响中起到部分中介作用。当算法推荐展现出对用户兴趣的精准理解时，用户更可能容忍算法的不透明性，提升整体评价，而单纯的技术优化效果有限。

Focusing on “algorithm aversion” within the context of news consumption, this study employs a controlled experiment to systematically examine the differences in news consumers’ subjective evaluations of algorithm-recommended versus human-recommended texts. It further explores the moderating effects of demographic characteristics and content preferences on recommendation preferences. A total of 800 participants were involved in the experiment, with three dimensions—willingness to accept recommendations, perceived personalization, and overall evaluation—measured using Likert scales. Data were analyzed using independent samples t-tests, multiple linear regressions, and path analyses. The statistical results reveal that human recommendations significantly outperform algorithmic recommendations overall, eliciting higher subjective satisfaction in the dimension of users’ overall evaluation, thereby verifying the existence of “algorithm aversion” in the Chinese news consumption sector. Although the algorithm group generally scored lower in the dimensions of recommendation willingness and perceived personalization, these differences did not reach statistical significance. Multiple linear regressions and interaction effect analyses indicate that variables such as occupation, gender, daily news reading time, and active news-seeking behavior significantly impact subjective evaluations across different recommendation modes. Notably, significant or marginally significant interaction effects were observed in certain dimensions for “occupation × recommendation mode” and “content preference × recommendation mode”, indicating that occupation and news content preferences moderate users' subjective evaluations of recommendation approaches. For instance, among users with a high preference for international news, the satisfaction gap between the algorithmic and human recommendation groups is narrowed. Furthermore, the three-dimensional path analysis demonstrates that overall evaluation partially mediates the effect of perceived personalization on the willingness to accept recommendations. The findings suggest that when algorithmic recommendations demonstrate a precise understanding of user interests, users are more likely to tolerate algorithmic opacity, which enhances their overall evaluation, whereas relying solely on technical optimization yields limited outcomes.