That is a very reasonable guess for what $\textrm{div}F$ should be, if you're thinking of it as a measure of ''how much $F$ is changing'' in some vague sense.
However, there is a more precise quantity of ''change" which we actually want the divergence to measure: at a given point, the divergence measures the flux of $F$ through a small sphere centered at that point.
In other words, suppose at each point in space there is a (flying) ant, and the vector field $F$ describes the velocity the ant's motion at each point. Then we can ask, after 1 second passes will there be more or less (fewer?) ants in a given region?
The answer is that there will be less ants if $\textrm{div}F > 0$, and more ants if $\textrm{div}F<0$. [see https://ka-perseus-images.s3.amazonaws.com/733847896ca352cc429608496cfd1a46a025ad30.svg]
Now, how does this idea translate into the standard formula for divergence? Well, we can test it on two simpler examples: consider
$$ F_1 = (x,0,0) \quad\text{and}\quad F_2 = (y,0,0). $$
If we think of these two vector fields as describing configurations of ant movements, in each case do the ants become more or less concentrated at a given point? [It may help to sketch out the vector fields, which I haven't done out of laziness.]
The standard divergence formula says $\textrm{div}F_1 = \frac{\partial x}{\partial x} = 1 > 0$ while $\textrm{div}F_2 = \frac{\partial y}{\partial x} = 0$. This should suggest that the ants moving according to $F_1$ will become more sparse, while the ants moving according to $F_2$ will not change their spatial density over time.
Does this match what you expect from looking at the two vector fields?
(Beyond looking at these two simple examples, I think the divergence formula comes out the way it does due to the magic of everything behaving linearly. But hopefully these two examples at least will illuminate the issue somwhat.)