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目的:探索大学生智能手机成瘾的潜在类别及其在心理和手机使用行为中的特征。方法:2023年7月，选用自编基本信息调查问卷、病人健康问卷抑郁量表(patient health questionnaire，PHQ-9)、UCLA孤独感量表简版(short-form of UCLA loneliness scale，ULS-8)、交往焦虑量表(interaction anxiousness scale，IAS)、错失焦虑量表(fear of missing out scale，FOMOs)、反刍思维量表(ruminative responses scale，RRS)、简版无聊倾向量表(short form version of boredom proneness scale，BPS-SF)、青少年气质量表中的意志控制分问卷、智能手机依赖量表简版(short version of smartphone addiction scale，SAS-SV)对北京市206名大学生进行横断面调查。采集手机使用时间和频率数据。运用SPSS 25.0进行统计描述和组间比较，使用Python 3.8软件K-means算法进行聚类分析，通过随机森林算法评估不同类别影响因素的重要性。结果:(1)手机成瘾[41.00(31.00，47.00)分]与抑郁[6.00(2.00，12.00)分]、孤独感[15.00(11.00，20.00)分]、社交焦虑[46.00(32.75，54.00)分]、错失焦虑[21.00(16.00，28.00)分]、反刍思维[26 .00(22.00，29.00)分]、无聊倾向[41.50(32.00，49.25)分]、平均每日手机使用时间[(513.30±213.29)min]、平均每次手机使用时间[6.60(3.68，14.09)min]呈正相关( r＝0.163～0.626，均 P<0.05)，与意志控制[49.00(44.00，59.00)分]呈负相关( r＝－0.613， P<0.01)。(2)聚类分析结果显示，大学生智能手机成瘾存在3种潜在类别：非成瘾组(32.04%，66/206)、无聊型手机成瘾组(26.70%，55/206)、多风险型手机成瘾组(41.26%，85/206)。(3)不同潜在类别在抑郁、孤独感、社交焦虑、反刍思维、无聊倾向、意志控制、手机成瘾上的得分均差异有统计学意义( H＝138.805，127.342，112.149，88.069，72.146，100.206，115.159，114.926；均 P<0.001)，在平均每日手机使用时间和次数、平均每次手机使用时间上均差异有统计学意义( F/ H＝7.548，9.332，16.086；均 P<0.01)。(4)随机森林算法分析结果显示，非成瘾组特征重要性前3位依次为：意志控制、社交焦虑、错失焦虑，特征重要性分别为0.33、0.23、0.15；无聊型手机成瘾组特征重要性前3位依次为：无聊倾向、错失焦虑、孤独感，特征重要性分别为0.35、0.20、0.15；多风险型手机成瘾组特征重要性前3位依次为：错失焦虑、反刍思维、无聊倾向，特征重要性分别为0.29、0.19、0.17。 结论:大学生智能手机成瘾存在3种潜在类别，各类别在不同心理因素和手机使用行为上差异显著，未来可根据不同大学生群体特点采取针对性心理干预措施。

Objective:To explore the latent patterns of smartphone addiction among college students and their characteristics of psychology and smartphone usage.Methods:In July 2023, a cross-sectional investigation was conducted among 206 college students in Beijing by using self-designed basic information questionnaire, patient health questionnaire (PHQ-9), the short-form of the UCLA loneliness scale (ULS-8), interaction anxiety scale(IAS), fear of missing out scale (FOMOs), ruminative response scale(RRS), the short form version of boredom proneness scale (BPS-SF), a sub-scale on effortful control from the early adolescent temperament measure, and the short version of smartphone addiction scale (SAS-SV), and data of weekly smartphone usage time and frequency were collected as well. SPSS 25.0 software was used for statistical description and inter group comparison.Cluster analysis was performed using Python 3.8 software K-means algorithm.The random forest algorithm was used to evaluate the importance of influencing factors in different categories.Results:(1) Smartphone addiction (41.00 (31.00, 47.00)) was positively correlated with depression(6.00 (2.00, 12.00)), loneliness (15.00 (11.00, 20.00)), social anxiety (46.00 (32.75, 54.00)), fear of missing out (21.00 (16.00, 28.00)), rumination (26.00 (22.00, 29.00)), boredom proneness (41.50 (32.00, 49.25)), average daily smartphone usage time ((513.30±213.29) min) and average time per smartphone use (6.60 (3.68, 14.09)) ( r=0.163~0.626, all P<0.05), while negatively correlated with effortful control (49.00 (44.00, 59.00)) ( r=-0.613, P<0.01). (2) Cluster analysis showed that there were three latent patterns of smartphone addiction among college students: non-addicted group (32.04%, 66/206), boredom-based smartphone addiction group (26.70%, 55/206), and multi-risk smartphone addiction group (41.26%, 85/206). (3) Significant differences were found among the different latent patterns in terms of depression, loneliness, social anxiety, rumination, boredom proneness, effortful control, smartphone addiction ( H=138.805, 127.342, 112.149, 88.069, 72.146, 100.206, 115.159, 114.926, all P<0.001), as well as average daily smartphone usage time and frequency, average smartphone usage time each time ( F/ H=7.548, 9.332, 16.086; all P<0.01). (4) Random forest algorithm analysis results showed that the top three feature importance rankings for the non-addicted group were effortful control, social anxiety, fear of missing out, with feature importance values of 0.33, 0.23 and 0.15. The top three feature importance rankings for the boredom-based smartphone addiction group were boredom proneness, fear of missing out, loneliness, with feature importance values of 0.35, 0.20 and 0.15. The top three feature importance rankings for the multi-risk smartphone addiction group were fear of missing out, rumination, boredom proneness, with feature importance values of 0.29, 0.19 and 0.17, respectively. Conclusions:There are three latent patterns of smartphone addiction among college students, and significant differences exist among these patterns in different psychology factors and characteristic of smartphone usage. In the future, targeted psychological interventions can be implemented based on the characteristics of different patterns of college students.