The rapid development of modern science and technology has spawned rich scientific topics to research and endless production of literature in them. Just like X-ray imaging in medicine, can we intuitively identify the development limit and internal evolution pattern of scientific topic from the relationship of massive knowledge? To answer this question, we collect 71431 seminal articles of topics that cover 16 disciplines and their citation data, and extracts the idea tree of each topic to restore the structure of the development of 71431 topic networks from scratch. We define the Knowledge Entropy (KE) metric, and the contribution of high knowledge entropy nodes to increase the depth of the idea tree is regarded as the basis for topic development. By observing X-ray images of topics, We find two interesting phenomena: (1) Even though the scale of topics may increase unlimitedly, there is an insurmountable cap of topic development: the depth of the idea tree does not exceed 6 jumps, which coincides with the classical Six Degrees of Separation! (2) It is difficult for a single article to contribute more than 3 jumps to the depth of its topic, to this end, the continuing increase in the depth of the idea tree needs to be motivated by the influence relay of multiple high knowledge entropy nodes. Through substantial statistical fits, we derive a unified quantitative relationship between the change in topic depth ${Delta D}^t(v)$ and the change in knowledge entropy over time ${KE}^tleft(vright)$ of the article $v$ driving the increase in depth in the topic: ${Delta D}^t(v) approx log frac{KE^{t}(v)}{left(t-t_{0}right)^{1.8803}}$ , which can effectively portray evolution patterns of topics and predict their development potential. The various phenomena found by scientific x-ray may provide a new paradigm for explaining and understanding the evolution of science and technology.
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