  In the artificial intelligence area, one of the ultimate goals is to make
computers understand human language and offer assistance. In order to achieve
this ideal, researchers of computer science have put forward a lot of models
and algorithms attempting at enabling the machine to analyze and process human
natural language on different levels of semantics. Although recent progress in
this field offers much hope, we still have to ask whether current research can
provide assistance that people really desire in reading and comprehension. To
this end, we conducted a reading comprehension test on two scientific papers
which are written in different styles. We use the semantic link models to
analyze the understanding obstacles that people will face in the process of
reading and figure out what makes it difficult for human to understand a
scientific literature. Through such analysis, we summarized some
characteristics and problems which are reflected by people with different
levels of knowledge on the comprehension of difficult science and technology
literature, which can be modeled in semantic link network. We believe that
these characteristics and problems will help us re-examine the existing machine
models and are helpful in the designing of new one.
