### Determination of Indicator Weight by Maximum Deviation Method  
Many methods are used to determine the indicator weight. The different sources of the original data that are used to calculate the weight can be divided into three categories; specifically, these are the Subjective, Objective and Combination Weight Methods.  
The Subjective Weight Method mainly includes the Expert Investigation Method and the Analytic Hierarchy Process, which originated earlier and is a more mature method. Its advantage is that experts can reasonably determine the order of attribute weights in accordance with actual decision-making problems and their own knowledge and experience. However, while this will not contradict the actual importance of attributes, decision-making or evaluation results will become subjective and arbitrary. However, it also increases the burden on decision-makers and shows great limitations in application.  
The Objective Weight Method mainly includes the Principal Component Analysis, Entropy, Deviation and Mean Square Deviation Methods. It is strongly objective and does not increase the burden on decision-makers. It has a strong mathematical theoretical basis, with one disadvantage--it relies on the original data.  
The Combination Weight Method organically integrates the subjective weight preference information given by the decision-maker with the objective decision matrix information--this largely incorporates the Multi-Objective Programming Method and ensures that the determined weight can simultaneously reflect the subjective and objective degree. The evaluation of science and technology talents seeks to resolve the problem of comparison and selection between talents and to distinguish them.  
The comprehensive evaluation value of science and technology talents that are calculated in the framework of the existing model should be set as far as possible to widen the gap, so this paper extracts the method of maximizing dispersion [22].  
The larger the dispersion, the greater the effect of the indicator on the scheme ordering and the higher the weight that is given. When the dispersion is smaller, the indicator will have a smaller effect on the ordering of the scheme and less weight will be given. \(\mathsf{e}_{\mathsf{j}}^{(\mathsf{P})}=1,2,\ \dots,\ n;\mathsf{p}=1,2,\ \dots,\ k\) is the \(\mathsf{j}\)-th indicator weight under the \(\mathsf{p}\)-th reference point. The idea of maximizing dispersion is drawn on to establish an optimization model M-1 that solves \(\mathsf{e}_{\mathsf{j}}^{(\mathsf{P})}\)[40].  
\[\left\{\begin{aligned} \max&=\sum_{\mathsf{j}=1}^{n}\! \sum_{\mathsf{i}=1}^{m}\!\sum_{\mathsf{j}=1}^{m}\left|\mathsf{v}_{\mathsf{ij} }^{(\mathsf{p})}(\otimes)-\mathsf{v}_{\mathsf{yj}}^{(\mathsf{p})}(\otimes) \right|\mathsf{e}_{\mathsf{j}}^{(\mathsf{p})}\\ \text{S.T.}&\quad 0\leq\mathsf{e}_{\mathsf{j}}^{( \mathsf{P})}\leq 1,\mathsf{j}\in n,\sum_{\mathsf{j}=1}^{n}\mathsf{e}_{ \mathsf{j}}^{(\mathsf{p})}=1\end{aligned}\right.\]  
The normalized uniform effect measure is multiplied by the indicator weight to obtain the uniform effect measure under different reference points.  
\[\mathsf{t}_{\mathsf{i}}^{(\mathsf{P})}(\otimes)=\sum_{\mathsf{j}=1}^{n}\! \left(\mathsf{v}_{\mathsf{ij}}^{(\mathsf{p})}(\otimes)\!*\mathsf{e}_{\mathsf{ j}}^{(\mathsf{p})}\right) \tag{12}\]