Atypical Hepatocellular Carcinoma (HCC) is very hard to distinguish from Focal Nodular Hyperplasia (FNH) in routine imaging. However little attention was paid to this problem. This paper proposes a novel liver tumor Computer-Aided Diagnostic (CAD) approach extracting spatio-temporal semantics for atypical HCC. With respect to useful diagnostic semantics, our model automatically calculates three types of semantic feature with equally down-sampled frames based on Contrast-Enhanced Ultrasound (CEUS). Thereafter, a Support Vector Machine (SVM) classifier is trained to make the final diagnosis. Compared with traditional methods for diagnosing HCC, the proposed model has the advantage of less computational complexity and being able to handle the atypical HCC cases. The experimental results show that our method obtained a pretty considerable performance and outperformed two traditional methods. According to the results, the average accuracy reaches 94.40%, recall rate 94.76%, F1-score value 94.62%, specificity 93.62% and sensitivity 94.76%, indicating good merit for automatically diagnosing atypical HCC cases.