There are some problems in existing rumor detection works， such as not fully integrating the information within propagation structure because of the deficiency of simultaneously capturing text semantic features and time periodic features in comment sequences and the inability to access the user personal profiles in a privacy-protected environment. To address the above problems， a Rumor Detection model fusing ambiguity in Comment Sequences and Generating User privacy features （RD-CSGU） was proposed. Text semantic features and time periodic features from different perspectives of comment sequences were comprehensively considered. Meanwhile， a heterogeneous network of rumor propagation for describing the social interaction relationship among users during the propagation process was constructed， based on which user privacy features were generated through a Generative Adversarial Network （GAN） based on the semantic relationships， overcoming the limitation of user personal profiles. The effectiveness of the proposed model was validated on Twitter15， Twitter16 and Weibo datasets. Compared with the suboptimal baseline model GLAN （Global-Local Attention Network）， RD-CSGU achieved improvements of 0.9， 2.2 and 1.8 percentage points in Accuracy （Acc）， as well as improvements of 2.6， 6.8 and 1.9 percentage points in TR （True Rumor）-F1 score. The results combined with those from ablation experiments and analysis of GAN-generated embeddings show that RD-CSGU can effectively detect rumor posts on social media platforms.