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This study to analyze the research trends of papers related to the diagnosis of autism spectrum disorder by using keyword network analysis. The analysis papers were 42 papers related to the diagnosis of autism disorder published from 2011-2020. As for the analysis data, general research trends, frequency analysis of major keywords, and network analysis were conducted using textome and UCINET. The results of this study are as follows. First, it was confirmed that a total of 42 studies were conducted from 2011 to 2020. Second, most of the papers related to the diagnosis of autism spectrum disorder were found to be academic journals in the field of special education. Third, as a result of examining the frequency of occurrence of major keywords in research related to the diagnosis of autism disorder, it was found in the order of ‘autism spectrum disorder’, ‘early diagnosis’, ‘focus’, ‘early screening’, and ‘infants’. Fourth, the result of TF-IDF weight analysis showed similar results to the order of appearance frequency. Fifth, in the N-gram analysis, the keywords that are highly related to the keyword ‘autism spectrum disorder’ were found to be in the order of ‘early diagnosis’, ‘early screening’, ‘children’, and ‘infants’. In addition, in the analysis of the semantic network, the keyword ‘autism spectrum disorder’ showed ‘early diagnosis’ and the highest degree of center of connection. Sixth, in the CONCOR analysis, the main clusters such as characteristics and support system for ‘autism spectrum disorder’, ‘diagnosis tool development’, and ‘early diagnosis’ were identified. This study analyzed the trends of research related to the diagnosis of autistic disorder, and discussed and suggested future research directions.