reduce analysis costs.

It is different from the numerical classification in similar articles \[[@B30]\]. The algorithm classification system is essentially reflecting the TGAM itself way of classification. As shown in [Figure 3](#fig3){ref-type="fig"}, if not done classification in advance, cannot explain why the null value area so coincidence slices the image into many areas. Therefore, we remove the null value areas and divide the rectangular regions which are composed of blue data points in [Figure 3](#fig3){ref-type="fig"}. In this way, it can be divided into seven levels ([Table 3](#tab3){ref-type="table"}) according to the abscissa (attention). The reliability of this attention level is proved in [Section 5](#sec5){ref-type="sec"}; it is crucial.

Among them, 0-4 and 98-100 actually have the attention value, but the algorithm still abandons them. For the former, this is because only 147 (1.52%) of the 9698 data sets are between 0 and 4, and more than half of them have nonzero poor signal. In addition, there are only two forms of these data: one is the continuous occurrence of multiple times with a huge difference with the data before and after. The other is the occurrence of only once with no connection with the data before and after as well. For the latter, only 100 will appear in the interval of 98-100, accounting for only 1.15% of the total number. The data will appear for several times in a row, and the difference with the previous data is more than 20 in most of times.

Combined with the fact that attention data rarely appear continuously and do not plunge or surge under regular circumstances, two areas can be abandoned.

5. Experimental Results {#sec5}
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In order to prove the reliability of this optimization algorithm, the following experiment is designed.

Record the change of the attention value within a certain period of time in two specific application scenarios, and select game and study as test scenarios to correspond to the situations of intermittent concentration and hi