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Facing the massive amount of data appearing on the web, automatic analysis tools have become essential for web users to discover valuable information online. Precise similarity measurement plays a decisive role in enabling analysis tools to acquire high-quality performances. Because different features contribute diversely to similarity calculation, it is necessary to utilize weight to measure feature's contribution and import it into similarity measurement. To accurately assign feature's weight, constrained data-pairs provided by users are usually imported into the weight evaluation procedure, whereas conventional plans all fail to consider two challenges: (a) asymmetrical distribution of constrained data-pairs, and (b) inconsistency contained by constrained data-pairs. If these two issues occur, conventional plans are incompetent at addressing them or are even unable to work. Thus, this paper proposes a novel constraint based weight evaluation to address these two issues. For the former, constrained data-pairs are partitioned into several equivalent classes, and distributing parameters are assigned to constrained data-pairs to balance their distributions. For the latter, constrained data-pairs are connected one after another, and belief values are thereby formed to indicate their probability of being inconsistent. Experimental results demonstrate that this type of evaluation is independent of any algorithm. With this evaluation, similarities can be calculated more accurately. [ABSTRACT FROM AUTHOR]