Web scraping is the process of automatically collecting information from the World Wide Web. It is a field with active developments, sharing a common goal with the semantic web vision, an ambitious initiative that still requires breakthroughs in text processing, artificial intelligence and human-computer interactions. It means extraction of content from different web pages using web scrapping and semantic illustration. Web Scrapping is a process of evocation of content from HTML pages and related to web indexing. A commonly used measure for tree similarity is the tree edit distance which easily can be extended to be a measure of how well a pattern can be matched in a tree. An obstacle for this approach is its time complexity, so we consider if faster algorithms for constrained tree edit distances are usable for web scraping, and to reduce the size of the tree representing the web page. Different applications of web scraping are used by current market to achieve best web scraping output, Like Web Data Extraction, Data Collection, Screen Scraping. Many different algorithms are used for web scraping like “tree pattern matching”, “tree mapping”, “approximate tree matching”. But in general “tree edit distance” algorithm is used. But with this algorithm many issues of incorrectness of data, low efficiency and higher time complexity have analyzed. In this research I am focus to improve the performance of tree edit distance problem. And I am also trying to focus on higher bound time complexity of this algorithm.