With the development of the information society, the scale of data has become larger and the types of data have become more abundant. Nowadays, data have become important strategic resources, which are the vital guarantees for scientific management for countries and enterprises. Nevertheless, with the increasing of data generated in social life, a large amount of dirty data come along with it, and data quality issue ensues. In the field of data science, it has always been a pain point that how to detect errors in an accurate and comprehensive manner. Although many traditional methods based on constraints or statistics have been widely used, they are usually limited by prior knowledge and labor cost. Recently, some novel methods detect errors by utilizing deep learning model to inference time series data or analyze context data and achieve better performance. However, these methods tend to be only applicable to specific areas or specific types of errors, which are not general enough for complex reality cases. Based on above observations, this study takes advantages of both traditional methods and deep learning model to propose a comprehensive error detection method (CEDM), which can deal with multiple type errors in multiple views. Firstly, under the view of patterns, basic detection rules can be constructed based on the statistical analysis with constraints from multiple dimensions, including attributes, cells, and tuples. After this, under the semantic view, data semantics are captured by word embedding and attribute relevance, cell dependency, and tuple similarity are analyzed. And hence, the basic rules can be extended and updated based on the semantic relations in different dimensions. Finally, the errors of multiple types could be detected comprehensively and accurately in multiple views. Extensive experiments on real and synthetic datasets demonstrate that the proposed method outperforms the state-of-the-art error detection methods and has higher generalization ability that can be applicable to multiple areas and multiple error types.