[cr0sh]

If it's possible to split the log out into a more granular format, beyond what fnbr has suggested, then it can potentially be used with more complex models; keep the issue as the "label", and the "system log" (or a hash representation?) as well - but if the log entry can be broken up into other data points, it can be useful in other ML methods.

Then again, if the log entry has a somewhat set length (or can be truncated), you could feed that in as the input to a CNN (one input node/neuron per character), and the output layer could consist of the issue labels. I'm not sure what if anything that could net you; perhaps an unknown log could be input on the trained network, and it could classify it to an existing issue?